공부/ML4T
CH07 선형 모델: 리스크 팩터에서 수익률 예측까지
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2023. 6. 30. 22:03
CH07 선형 모델: 리스크 팩터에서 수익률 예측까지¶
선형 모델은 가장 유용한 가설 클래스 중 하나를 나타낸다.
이후 수많은 확장이 선형회귀 모델과 최소 자승법(OLS)을 적용해 파라미터를 학습시켰다.
- 일반화 선형 모델(GLM): 정규 분포 이외의 오차 분포를 암시하는 반응 변수를 허용함으로써 적용의 범위를 확장했다.
- 강건한 추정방법(robust estimation method): 시간에 따른 상관관계나 관측치 간 상관관계로 인해 데이터가 기준 가정을 위반하는 경우 통계적 추론이 가능하다.
- 수축 방법(shrinkage method): 선형 모델의 예측 성과를 향상시키는 것을 목표로 한다.
분류에는 방향성 가격 예측이 포함된다.
7장에서 다루는 내용
- 선형 회귀 분석의 원리와 가정은 무엇인가?
- 선형 회귀 모델 훈련과 진단은 어떻게 하는가?
- 선형 회귀 분석을 사용해 주식 수익률은 어떻게 예측하는가?
- 규제화를 사용해 예측 성과를 어떻게 개선하는가?
- 로지스틱 회귀 분석의 작동 원리는 무엇인가?
- 회귀 분석을 분류 문제로 어떻게 변환시키는가?
추론에서 예측까지¶
표본으로부터 추정된 회귀 모수에 근거해 모집단의 실제 선형 관계에 대한 통계적 결론을 이끌어 내려면 오차의 통계적 특성에 관한 가정을 추가해야 한다.
- 오차 분포가 관측치 전체에서 동일하다.
- 오차는 서로 독립적이다.
독립적이고 동일하게 분포된 오차의 가정은 공분산 행렬이 오차 분산을 나타내는 상수를 곱한 항등 행렬임을 의미한다.
→ 최소 자승법(OLS method)이 편향이 없고, 효율적인 추정치를 제공하는 것을 보장하며, 가장 낮은 표본 오차를 달성한다는 것을 의미한다.
수축 방법은 선형 목적 함수에 패널티 항을 추가하는 규제화를 적용해 모델 복잡성을 감소시킨다.
- 패널티는 계수의 절대적 크기의 양(+)의 관련성이 있어 이러한 계수는 기준 사례에 비례해 수축된다.
베이스라인 모델: 다중 선형 회귀 모델¶
모델을 만드는 방법¶
다중 회귀 모델은 하나의 연속 결과 변수와 어떤 유형이든 가능하지만 전처리가 필요한 입력 변수 p 사이의 선형 함수 관계로 정의된다.
$y = f(x) + ϵ \\y = β₀ + β₁x₁ + β₂x₂ + ... + βₚxₚ + ϵ \\y = β₀ + \sum_{j=1}^p β_jx_j + ϵ$
- y는 종속 변수(출력)입니다.
- x₁, x₂, ..., xₚ는 독립 변수(입력)입니다.
- β₀, β₁, β₂, ..., βₚ는 회귀 계수입니다.
- ϵ는 오차 항입니다.
위의 수식은 입력 벡터 X^T=(x₁, ..., xₚ)와 회귀 계수 벡터 β=(β₀, β₁, β₂, ..., βₚ)를 내적한 결과에 오차 항 ϵ를 더한 형태로 표현됩니다.
모델을 훈련하는 방법¶
데이터에서 모델 파라미터(모델 모수)를 학습할 때 사용할 수 있는 방법
- 최소 자승법(OLS)
- 최대 우도 추정(MLE)
- 확률적 경사 하강법(SGD)
최소 자승법: 초평면을 데이터에 맞추는 방법¶
최소 자승법 (Ordinary Least Squares, OLS): OLS는 주어진 데이터에 대해 잔차(오차)의 제곱합을 최소화하여 모델 파라미터를 추정하는 방법입니다.
여기서,
는 관측된 종속 변수 값입니다.
는 i번째 데이터 포인트의 독립 변수 벡터입니다.
는 추정하려는 회귀 계수(모델 파라미터) 벡터입니다.
은 데이터 포인트의 총 개수입니다.
OLS는 잔차(오차)의 제곱합을 최소화하여 모델 파라미터를 추정하는 기법입니다. 목표는 관측된 종속 변수 값과 예측 값의 차이(잔차)를 최소화하는 최적의 회귀 계수를 찾는 것입니다. OLS는 행렬 연산을 사용하여 정규 방정식을 풀어 추정된 회귀 계수를 구할 수 있습니다.
최대 우도 추정¶
최대 우도 추정 (Maximum Likelihood Estimation, MLE): MLE는 주어진 데이터에서 관측된 종속 변수의 확률 분포 모수를 추정하는 방법 중 하나입니다.
여기서,
는 i번째 데이터 포인트의 독립 변수 값들을 포함한 벡터입니다.
는 추정하려는 모델 파라미터(모수) 벡터입니다.
는 주어진 모델 파라미터(모수)
MLE는 관측된 데이터를 가장 잘 설명하는 모델 파라미터(모수)를 추정하는 방법입니다. 주어진 데이터에서 관측된 종속 변수의 확률 분포를 모델링하고, 주어진 모델 파라미터 아래에서 관측된 종속 변수의 확률을 최대화하는 파라미터 값을 찾습니다. 이때, 데이터가 독립적으로 추출되었다고 가정합니다. 최적화 알고리즘을 사용하여 MLE를 수행할 수 있습니다.
경사하강법¶
확률적 경사 하강법 (Stochastic Gradient Descent, SGD) SGD는 데이터 세트의 한 데이터 포인트를 사용하여 모델 파라미터를 업데이트하는 반복적인 최적화 알고리즘입니다.
여기서,
는 학습률(learning rate)로, 각 업데이트에서의 보정 정도를 조절하는 하이퍼파라미터입니다.
는 손실 함수(여기서는 잔차의 제곱)에 대한 기울기(gradient) 연산을 나타냅니다.
SGD는 각 반복 단계에서 하나의 데이터 포인트를 사용하여 모델 파라미터를 업데이트하는 방법입니다. 잔차의 제곱을 최소화하기 위해 손실 함수의 기울기를 계산하고, 학습률을 곱하여 모델 파라미터를 업데이트합니다. 이 과정을 데이터 세트 전체에 대해 반복하면서 모델 파라미터를 조금씩 조정하여 최적의 파라미터 값을 찾습니다. SGD는 큰 데이터셋에서도 효과적으로 사용될 수 있으며, 온라인 학습(online learning)에 적합한 알고리즘입니다.
가우스-마르코프 정리¶
가우스-마르코프 정리(GMT)는 모델 파라미터 β의 불편 추정치를 생성하는 데 최소 자승법(OLS)이 필요한 가정과 이러한 추정치가 횡단면 데이터에 대해 모든 선형 모델 중에 가장 낮은 표준 오차를 갖는 경우를 정의한다.
- 모집단에서 선형성은 $y = β₀ + β₁x_x + β₂x_x + ... + β_kx_x + ϵ $ 상태를 유지한다. $β_i$는 알 수 없지만 상수이며, ϵ는 확률 오차다.
- 입력 변수에 대한 데이터 $x_1, ..., x_k$는 모집단에서 추출한 확률 표본이다.
- 완전 공선성은 없다. 즉, 입력 변수 사이에 정확한 선형 관계는 없다.
- 오차 ϵ은 어떤 입력을 하더라도 0의 조건부 평균을 가진다. 즉, $E[ϵ_1, ..., x_k] = 0$이다.
- 등분산성, 오차 항 ϵ은 입력 변수에 따라 일정한 분산을 가진다. 즉 $E[ϵ_1, ..., x_k]=σ^2$이다.
통계적 추론을 수행하는 방법¶
- p-값: 검정 통계량에 대한 귀무가설을 공식화하고 가설이 정확하다는 가정하에 표본이 주어진 경우, 이 통계량에 대한 실제 값을 관찰할 확률
검정 통계량의 분포적 특징
파라미터 추정치는 다변량 정규 분포를 따른다. 즉, 다음과 같다.
$$\hat{\beta} \sim \mathcal{N}(\beta, (X^TX)^{-1})σ$$
- GMT 1~5에서 모수 추정치는 이미 불편이고, 다음 식을 사용해 일정한 오차 분산을 갖는 불편 추정치를 얻을 수 있다. $$\hat{\sigma} = \frac{1}{N-p-1}∑_{i=2}^N (y_i-\hat{y}_i)^2$$
- 개별 계수 $β_j$에 대한 가설 검증의 t 통계량은 $ t_j = \frac{\hat{\beta}_j}{\hat{\sigma}\sqrt{v_j}} \sim t_{N-p-1} $이며, 자유도 N-p-1을 가진 t 분포를 따른다. 여기서 $v_j$는 $(X^RX)^{-1}$의 대각행렬에서 j번째 요소다.
- t 분포는 정규 분포로 수렴되며 정규 분포의 97.5 분위수는 1.96이므로 모수 추정치의 95% 신뢰 구간에서 유용한 어림 법칙은 $\hat{\beta} \pm 2 \cdot \text{se}(\hat{\beta}) $이다. 0을 포함하는 구간은 실제 모수가 0이므로 모델과 관련 없다는 귀무가설을 기각할 수 없음을 의미한다.
- F 통계량은 전체 회귀가 유의한지 여부를 포함해 여러 모수에 대한 제한 검정을 허용한다. 추가 변수로 인한 RSS의 변화(감소)를 측정한다.
- 마지막으로 라그랑지 승수 검증은 다중 제약을 테스트하기 위한 F 검증의 대안이다.
문제를 진단하고 해결하는 방법¶
진단은 결과를 해석하고 통계적 추론을 수행할 때 모델의 가정을 검증하고 잘못된 결론을 방지한다.
적합도¶
- 적합도 측도는 모델이 결과의 변동을 얼마나 잘 설명하는지 평가한다.
- 결정 계수(R^2)
- 아카이케 정보 기준(AIC)
- 베이지안 정보 기준(BIC)
이분산성¶
- 이분산성은 잔차 분산이 일정하지 않고 관측치에 걸쳐 다른 경우 발생한다.
- 잔차 분산이 입력 변수와 양(+)의 상관을 보일 경우, 즉 평균으로부터 거리가 먼 입력값에 대해 오차가 큰 경우 OLS 표준 오차 추정치는 너무 낮게 된다. 따라서 t 통계량은 부풀려져 실제로 존재하지 않는 관계에 대한 잘못된 발견이 이어지게 된다.
- 임의로 추정되는 잔차의 체계적인 패턴은 오차가 다양한 대안에 대해 동질적이라는 귀무가설을 통계적으로 검증하는 것
- 브로쉬-파간
- 화이트 검증
- 이분산성에 대한 OLS 추정치를 정정하는 방법
- 조정된 표준 오차(=화이트 표준 오차): 소위 샌드위치 추정치를 사용해 오차 분산을 계산할 때 이분산성을 고려한다.
- 클러스터링된 표준 오차: 데이터가 동질적으로 구별되는 그룹에 있다고 가정하지만 오차 분산은 그룹마다 다르다.
- OLS에 대한 대안: 서로 다른 가정을 사용해 오차 공분산 행렬을 추정한다.
- 가중 최소 자승법(WLS): 공분산 행렬이 OLS와 같이 대각선 항목만 포함하는 이분산 오차의 경우 이제 항목이 변경될 수 있다.
- 달성 가능한 일반화 최소 자승법(GLSAR): 자기 회귀 AR(p) 과정을 따르는 자기상관된 오차에 대해 사용
- 일반화 최소 자승법(GLS): 임의 공분산 행렬 구조의 경우 이분산성이나 계열 상관이 존재하는 경우에 효율적이고 불편성이 없는, 즉 치우치지 않은 추정치를 산출
계열 상관¶
- 계열 상관(Serial correlation)은 선형 회귀 분석으로 생성된 연속적인 잔차에 상관관계가 있다는 것을 의미하며, 네 번째 GMT 가정을 위반한다.
- 양(+)의 계열 상관은 표준오차가 과소 추정되고 t 통계량이 부풀려져 무시될 경우 잘못된 발견으로 이어진다.
- 표준 오차를 계산할 때 계열 상관을 정확하게 교정하는 절차
- 더빈-왓슨 통계: 계열 상관을 진단한다.
다중 공선성¶
- 다중 공선성(multicollinearity)은 두 개 이상의 독립 변수가 높은 상관관계를 가질 때 발생한다.
문제
- 종속 변수에 영향을 미치는 팩터를 결정하기 어렵게 한다.
- 개별 p-값은 오인의 여지가 있다. 실제 변수가 중요하더라도 p-값은 높을 수 있다.
- 회귀 계수에 대한 신뢰 구간은 0을 포함하더라도 지나치게 과도해질 수 있으므로 결과에 대한 독립 변수의 영향을 결정하는 데 복잡하다.
다중 공선성을 교정하는 공식이나 이론적 해결책은 없으며, 하나 이상의 상관된 입력 변수를 제거하거나 표본 크기를 증가시키는 방법이 있다.
실제로 선형 회귀를 실행하는 방법¶
- 01_linear_regression_intro.ipynb
Linear Regression - Introduction¶
Linear regression relates a continuous response (dependent) variable to one or more predictors (features, independent variables), using the assumption that the relationship is linear in nature:
- The relationship between each feature and the response is a straight line when we keep other features constant.
- The slope of this line does not depend on the values of the other variables.
- The effects of each variable on the response are additive (but we can include new variables that represent the interaction of two variables).
In other words, the model assumes that the response variable can be explained or predicted by a linear combination of the features, except for random deviations from this linear relationship.
Imports & Settings¶
In [1]:
import warnings
warnings.filterwarnings('ignore')
In [2]:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from sklearn.linear_model import SGDRegressor
from sklearn.preprocessing import StandardScaler
In [3]:
sns.set_style('whitegrid')
pd.options.display.float_format = '{:,.2f}'.format
Simple Regression¶
Generate random data¶
In [4]:
x = np.linspace(-5, 50, 100)
y = 50 + 2 * x + np.random.normal(0, 20, size=len(x))
data = pd.DataFrame({'X': x, 'Y': y})
ax = data.plot.scatter(x='X', y='Y', figsize=(14, 6))
sns.despine()
plt.tight_layout()
Our linear model with a single independent variable on the left-hand side assumes the following form:
$$y = \beta_0 + \beta_1 X_1 + \epsilon$$$\epsilon$ accounts for the deviations or errors that we will encounter when our data do not actually fit a straight line. When $\epsilon$ materializes, that is when we run the model of this type on actual data, the errors are called residuals.
Estimate a simple regression with statsmodels¶
The upper part of the summary displays the dataset characteristics, namely the estimation method, the number of observations and parameters, and indicates that standard error estimates do not account for heteroskedasticity.
The middle panel shows the coefficient values that closely reflect the artificial data generating process. We can confirm that the estimates displayed in the middle of the summary result can be obtained using the OLS formula derived previously:
In [5]:
X = sm.add_constant(data['X'])
model = sm.OLS(data['Y'], X).fit()
print(model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Y R-squared: 0.752
Model: OLS Adj. R-squared: 0.749
Method: Least Squares F-statistic: 296.6
Date: Sat, 24 Jun 2023 Prob (F-statistic): 2.11e-31
Time: 15:44:21 Log-Likelihood: -430.12
No. Observations: 100 AIC: 864.2
Df Residuals: 98 BIC: 869.4
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 48.9314 3.107 15.747 0.000 42.765 55.098
X 1.9369 0.112 17.223 0.000 1.714 2.160
==============================================================================
Omnibus: 0.276 Durbin-Watson: 2.108
Prob(Omnibus): 0.871 Jarque-Bera (JB): 0.289
Skew: 0.120 Prob(JB): 0.865
Kurtosis: 2.892 Cond. No. 47.6
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Verify calculation¶
In [6]:
beta = np.linalg.inv(X.T.dot(X)).dot(X.T.dot(y))
pd.Series(beta, index=X.columns)
Out[6]:
const 48.93 X 1.94 dtype: float64
Display model & residuals¶
In [7]:
data['y-hat'] = model.predict()
data['residuals'] = model.resid
ax = data.plot.scatter(x='X', y='Y', c='darkgrey', figsize=(14,6))
data.plot.line(x='X', y='y-hat', ax=ax);
for _, row in data.iterrows():
plt.plot((row.X, row.X), (row.Y, row['y-hat']), 'k-')
sns.despine()
plt.tight_layout();
Multiple Regression¶
For two independent variables, the model simply changes as follows:
$$y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon$$Generate new random data¶
In [9]:
## Create data
size = 25
X_1, X_2 = np.meshgrid(np.linspace(-50, 50, size), np.linspace(-50, 50, size), indexing='ij')
data = pd.DataFrame({'X_1': X_1.ravel(), 'X_2': X_2.ravel()})
data['Y'] = 50 + data.X_1 + 3 * data.X_2 + np.random.normal(0, 50, size=size**2)
## Plot
three_dee = plt.figure(figsize=(15, 5)).add_subplot(projection='3d')
# ERROR SOLUTION: https://stackoverflow.com/questions/76047803/typeerror-figurebase-gca-got-an-unexpected-keyword-argument-projection
three_dee.scatter(data.X_1, data.X_2, data.Y, c='g')
sns.despine()
plt.tight_layout();
In [10]:
X = data[['X_1', 'X_2']]
y = data['Y']
Estimate multiple regression model with statsmodels¶
패널의 우측 상단에는 방금 논의한 적합도 지표와 함께 모든 계수가 0이고 관련이 없다는 가설을 기각하는 F-검정이 표시됩니다. 마찬가지로 t-통계량은 절편과 기울기 계수 모두가 당연히 매우 유의미함을 나타냅니다.
요약의 하단 부분에는 잔차 진단 결과가 표시됩니다. 좌측 패널은 정규성 가설을 검정하는 데 사용되는 비대칭도(skewness)와 첨도(kurtosis)를 표시합니다. Omnibus 테스트와 Jarque-Bera 테스트는 잔차가 정규분포를 따른다는 귀무가설을 기각하지 못합니다. Durbin-Watson 통계량은 잔차의 직렬 상관성을 검정하며, 2에 가까운 값은 2개의 매개변수와 625개의 관측치에 대한 직렬 상관성이 없다는 가설을 기각하지 못합니다.
마지막으로, 조건수는 다중공선성에 대한 증거를 제공합니다. 이는 입력 데이터를 포함하는 설계 행렬의 가장 큰 고유값과 가장 작은 고유값의 제곱근의 비율입니다. 30 이상의 값은 회귀분석에 중요한 다중공선성이 존재할 수 있다는 것을 나타냅니다.
In [11]:
X_ols = sm.add_constant(X)
model = sm.OLS(y, X_ols).fit()
print(model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Y R-squared: 0.796
Model: OLS Adj. R-squared: 0.795
Method: Least Squares F-statistic: 1213.
Date: Sat, 24 Jun 2023 Prob (F-statistic): 2.15e-215
Time: 15:45:37 Log-Likelihood: -3314.4
No. Observations: 625 AIC: 6635.
Df Residuals: 622 BIC: 6648.
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 56.7089 1.950 29.086 0.000 52.880 60.538
X_1 1.0756 0.065 16.576 0.000 0.948 1.203
X_2 3.0098 0.065 46.383 0.000 2.882 3.137
==============================================================================
Omnibus: 3.481 Durbin-Watson: 1.964
Prob(Omnibus): 0.175 Jarque-Bera (JB): 3.532
Skew: -0.163 Prob(JB): 0.171
Kurtosis: 2.828 Cond. No. 30.0
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Verify computation¶
In [12]:
beta = np.linalg.inv(X_ols.T.dot(X_ols)).dot(X_ols.T.dot(y))
pd.Series(beta, index=X_ols.columns)
Out[12]:
const 56.71 X_1 1.08 X_2 3.01 dtype: float64
Save output as image¶
In [13]:
plt.rc('figure', figsize=(12, 7))
plt.text(0.01, 0.05, str(model.summary()), {'fontsize': 14}, fontproperties = 'monospace')
plt.axis('off')
plt.tight_layout()
plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)
# plt.savefig('multiple_regression_summary.png', bbox_inches='tight', dpi=300);
Display model & residuals¶
The following diagram illustrates the hyperplane fitted by the model to the randomly generated data points
In [15]:
three_dee = plt.figure(figsize=(15, 5)).add_subplot(projection='3d')
three_dee.scatter(data.X_1, data.X_2, data.Y, c='g')
data['y-hat'] = model.predict()
to_plot = data.set_index(['X_1', 'X_2']).unstack().loc[:, 'y-hat']
three_dee.plot_surface(X_1, X_2, to_plot.values, color='black', alpha=0.2, linewidth=1, antialiased=True)
for _, row in data.iterrows():
plt.plot((row.X_1, row.X_1), (row.X_2, row.X_2), (row.Y, row['y-hat']), 'k-');
three_dee.set_xlabel('$X_1$');three_dee.set_ylabel('$X_2$');three_dee.set_zlabel('$Y, \hat{Y}$')
sns.despine()
plt.tight_layout();
Additional diagnostic tests
Stochastic Gradient Descent Regression¶
The sklearn library includes an SGDRegressor model in its linear_models module. To learn the parameters for the same model using this method, we need to first standardize the data because the gradient is sensitive to the scale.
Prepare data¶
The gradient is sensitive to scale and so is SGDRegressor. Use the StandardScaler or scale to adjust the features.
We use StandardScaler() for this purpose that computes the mean and the standard deviation for each input variable during the fit step, and then subtracts the mean and divides by the standard deviation during the transform step that we can conveniently conduct in a single fit_transform() command:
In [16]:
scaler = StandardScaler()
X_ = scaler.fit_transform(X)
Configure SGDRegressor¶
Then we instantiate the SGDRegressor using the default values except for a random_state setting to facilitate replication:
In [19]:
sgd = SGDRegressor(loss='squared_error', # squared_loss -> squared_error
fit_intercept=True,
shuffle=True,
random_state=42,
learning_rate='invscaling',
eta0=0.01,
power_t=0.25)
Fit Model¶
Now we can fit the sgd model, create the in-sample predictions for both the OLS and the sgd models, and compute the root mean squared error for each:
In [20]:
# sgd.n_iter = np.ceil(10**6 / len(y))
sgd.fit(X=X_, y=y)
Out[20]:
SGDRegressor(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SGDRegressor(random_state=42)
As expected, both models yield the same result. We will now take on a more ambitious project using linear regression to estimate a multi-factor asset pricing model.
In [21]:
coeffs = (sgd.coef_ * scaler.scale_) + scaler.mean_
pd.Series(coeffs, index=X.columns)
Out[21]:
X_1 969.73 X_2 2,714.32 dtype: float64
In [22]:
resids = pd.DataFrame({'sgd': y - sgd.predict(X_),
'ols': y - model.predict(sm.add_constant(X))})
In [23]:
resids.pow(2).sum().div(len(y)).pow(.5)
Out[23]:
sgd 48.63 ols 48.62 dtype: float64
In [24]:
resids.plot.scatter(x='sgd', y='ols')
sns.despine()
plt.tight_layout();
선형 팩터 모델을 구축하는 방법¶
알고리듬 트레이딩 전략은 이러한 수익률의 주요 동인을 나타내는 리스크의 원천과 자산 수익률 간의 관계를 계량화하고자 팩터 모델을 사용한다.
각 팩터 리스크는 프리미엄이 붙이며, 총 자산 수익률은 이러한 리스크 프리미엄의 가중 평균한 것으로 간주할 수 있다.
CAPM에서 파마-프렌치 5 팩터 모델까지¶
자본 자산 가격 결정 모델(CAPM)은 무위험 이자율 대비 전체 시장의 초과 기대 수익률인 단일 팩터에 대한 개별 익스포저를 사용해 모든 N개 자산의 기대 수익률을 설명한다.
CAPM 모델은 다음과 같은 선형 형태를 취한다.
$$E[r_i]=\alpha_i+r_f+β_i(E[r_m]-r_f)$$
파마-프렌치 리스크 팩터는 주어진 리스크 팩터를 반영하는 메트릭에 따라 높은 값이나 낮은 값을 가진 다양한 포트폴리오에 대한 수익률 차이로 계산된다.
리스크 팩터와 관련된 메트릭
- 시작 규모: 시장 지분(ME)
- 가치: 주식의 장부 가치(BE_를 ME로 나눈 값
- 영업 수익성(OP): 매출/자산 판매에서 비용을 뺀 금액
- 투자: 투자/자산
02_fama_macbeth.ipynb
How to build a linear factor model¶
Algorithmic trading strategies use linear factor models to quantify the relationship between the return of an asset and the sources of risk that represent the main drivers of these returns. Each factor risk carries a premium, and the total asset return can be expected to correspond to a weighted average of these risk premia.
There are several practical applications of factor models across the portfolio management process from construction and asset selection to risk management and performance evaluation. The importance of factor models continues to grow as common risk factors are now tradeable:
- A summary of the returns of many assets by a much smaller number of factors reduces the amount of data required to estimate the covariance matrix when optimizing a portfolio
- An estimate of the exposure of an asset or a portfolio to these factors allows for the management of the resultant risk, for instance by entering suitable hedges when risk factors are themselves traded
- A factor model also permits the assessment of the incremental signal content of new alpha factors
- A factor model can also help assess whether a manager's performance relative to a benchmark is indeed due to skill in selecting assets and timing the market, or if instead, the performance can be explained by portfolio tilts towards known return drivers that can today be replicated as low-cost, passively managed funds without incurring active management fees
Imports & Settings¶
In [25]:
import warnings
warnings.filterwarnings('ignore')
In [27]:
!pip install linearmodels
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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In [28]:
import pandas as pd
import numpy as np
from statsmodels.api import OLS, add_constant
import pandas_datareader.data as web
from linearmodels.asset_pricing import LinearFactorModel
import matplotlib.pyplot as plt
import seaborn as sns
In [29]:
sns.set_style('whitegrid')
Get Data¶
Fama and French make updated risk factor and research portfolio data available through their website, and you can use the pandas_datareader package to obtain the data.
리스크 팩터(Risk Factors) 입수¶
특히, 우리는 주식을 먼저 세 가지 크기 그룹으로 분류한 후 나머지 세 개의 기업 특정 요소에 대해 각각 두 개씩 분류하여 얻은 다섯 가지 Fama-French 요인을 사용할 것입니다.
따라서 이러한 요인은 크기와 시장가치비율, 크기와 영업이익성, 그리고 크기와 투자에 따라 형성된 세 개의 가중치 기반 포트폴리오 세트로 구성됩니다. 위표에서 설명한 대로 포트폴리오 (PF)의 평균 수익률로 계산된 리스크 요인 값이 됩니다.
| 레이블 | 이름 | 설명 |
|---|---|---|
| SMB | Small Minus Big | 아홉 개의 소형 주식 포트폴리오의 평균 수익률에서 아홉 개의 대형 주식 포트폴리오의 평균 수익률을 뺀 값 |
| HML | High Minus Low | 두 개의 가치 포트폴리오의 평균 수익률에서 두 개의 성장 포트폴리오의 평균 수익률을 뺀 값 |
| RMW | Robust minus Weak | 두 개의 견고한 영업이익성 포트폴리오의 평균 수익률에서 두 개의 약한 영업이익성 포트폴리오의 평균 수익률을 뺀 값 |
| CMA | Conservative Minus Aggressive | 두 개의 보수적인 투자 포트폴리오의 평균 수익률에서 두 개의 공격적인 투자 포트폴리오의 평균 수익률을 뺀 값 |
| Rm-Rf | 시장 초과 수익률 | 미국에 상장된 NYSE, AMEX, 또는 NASDAQ의 모든 기업에 대한 가중 평균 수익률에서 월 초에 '좋은' 데이터를 가진 t-1 개월 미국 국채 수익률을 뺀 값 |
위의 표에서는 다양한 개념과 리스크 팩터 계산 컬럼을 제공하고 있습니다.
파마-프렌치 5 요인은 크기와 시장가치비율을 기반으로 형성된 6개의 가중치 기반 포트폴리오, 크기와 영업이익성을 기반으로 형성된 6개의 가중치 기반 포트폴리오, 그리고 크기와 투자를 기반으로 형성된 6개의 가중치 기반 포트폴리오에 기반합니다.
We will use returns at a monthly frequency that we obtain for the period 2010 – 2017 as follows:
In [30]:
ff_factor = 'F-F_Research_Data_5_Factors_2x3'
ff_factor_data = web.DataReader(ff_factor, 'famafrench', start='2010', end='2017-12')[0]
ff_factor_data.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 96 entries, 2010-01 to 2017-12 Freq: M Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Mkt-RF 96 non-null float64 1 SMB 96 non-null float64 2 HML 96 non-null float64 3 RMW 96 non-null float64 4 CMA 96 non-null float64 5 RF 96 non-null float64 dtypes: float64(6) memory usage: 5.2 KB
In [31]:
ff_factor_data.describe()
Out[31]:
| Mkt-RF | SMB | HML | RMW | CMA | RF | |
|---|---|---|---|---|---|---|
| count | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 |
| mean | 1.16 | 0.06 | -0.05 | 0.13 | 0.05 | 0.01 |
| std | 3.58 | 2.30 | 2.20 | 1.58 | 1.41 | 0.02 |
| min | -7.89 | -4.58 | -4.70 | -3.88 | -3.24 | 0.00 |
| 25% | -0.92 | -1.67 | -1.67 | -1.08 | -0.95 | 0.00 |
| 50% | 1.23 | 0.20 | -0.28 | 0.21 | 0.01 | 0.00 |
| 75% | 3.20 | 1.58 | 1.21 | 1.23 | 0.93 | 0.01 |
| max | 11.35 | 7.04 | 8.19 | 3.48 | 3.69 | 0.09 |
Portfolios¶
파마와 프렌치는 또한 요인 노출의 추정 및 특정 시간 기간에 시장에서 제공되는 리스크 프리미어의 가치를 설명할 수 있는 다양한 포트폴리오를 제공합니다. 우리는 월간 주파수로 17개 산업 포트폴리오의 패널 데이터를 사용할 것입니다.
요인 모형은 초과 수익률(excess returns)로 작동하므로 수익률에서 위험 없는 이자율을 차감할 것입니다.
In [32]:
ff_portfolio = '17_Industry_Portfolios'
ff_portfolio_data = web.DataReader(ff_portfolio, 'famafrench', start='2010', end='2017-12')[0]
ff_portfolio_data = ff_portfolio_data.sub(ff_factor_data.RF, axis=0)
ff_portfolio_data.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 96 entries, 2010-01 to 2017-12 Freq: M Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Food 96 non-null float64 1 Mines 96 non-null float64 2 Oil 96 non-null float64 3 Clths 96 non-null float64 4 Durbl 96 non-null float64 5 Chems 96 non-null float64 6 Cnsum 96 non-null float64 7 Cnstr 96 non-null float64 8 Steel 96 non-null float64 9 FabPr 96 non-null float64 10 Machn 96 non-null float64 11 Cars 96 non-null float64 12 Trans 96 non-null float64 13 Utils 96 non-null float64 14 Rtail 96 non-null float64 15 Finan 96 non-null float64 16 Other 96 non-null float64 dtypes: float64(17) memory usage: 13.5 KB
In [33]:
ff_portfolio_data.describe()
Out[33]:
| Food | Mines | Oil | Clths | Durbl | Chems | Cnsum | Cnstr | Steel | FabPr | Machn | Cars | Trans | Utils | Rtail | Finan | Other | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 | 96.00 |
| mean | 1.05 | 0.20 | 0.60 | 1.40 | 1.15 | 1.30 | 1.19 | 1.74 | 0.56 | 1.35 | 1.22 | 1.28 | 1.46 | 0.90 | 1.23 | 1.25 | 1.29 |
| std | 2.80 | 7.90 | 5.48 | 5.02 | 5.16 | 5.59 | 3.14 | 5.24 | 7.39 | 4.69 | 4.80 | 5.72 | 4.14 | 3.23 | 3.51 | 4.84 | 3.70 |
| min | -5.17 | -24.38 | -11.68 | -10.00 | -13.16 | -17.39 | -7.15 | -14.16 | -20.49 | -11.96 | -9.07 | -11.65 | -8.56 | -6.99 | -9.18 | -11.14 | -7.89 |
| 25% | -0.79 | -5.84 | -3.12 | -1.87 | -2.10 | -1.44 | -0.85 | -2.41 | -4.39 | -1.45 | -2.06 | -1.25 | -0.81 | -0.74 | -0.95 | -1.46 | -1.09 |
| 50% | 0.92 | -0.44 | 0.98 | 1.16 | 1.23 | 1.44 | 1.46 | 2.17 | 0.66 | 1.48 | 1.52 | 0.64 | 1.48 | 1.24 | 0.86 | 1.91 | 1.66 |
| 75% | 3.19 | 5.73 | 4.15 | 3.86 | 4.16 | 4.44 | 3.30 | 5.56 | 4.21 | 3.84 | 4.58 | 4.80 | 4.24 | 2.96 | 3.37 | 4.10 | 3.48 |
| max | 6.67 | 21.94 | 15.94 | 17.19 | 16.61 | 18.37 | 8.26 | 15.51 | 21.35 | 17.66 | 14.75 | 20.86 | 12.98 | 7.84 | 12.44 | 13.41 | 10.77 |
Equity Data¶
In [34]:
from google.colab import drive
drive.mount('/content/drive') # 구글 드라이브를 사용하는 경우
Mounted at /content/drive
In [35]:
!unzip /content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/WIKI_PRICES.zip
Archive: /content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/WIKI_PRICES.zip inflating: WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv
In [36]:
!mv WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv wiki_prices.csv
In [43]:
DATA_STORE = 'assets.h5'
In [44]:
df = (pd.read_csv('wiki_prices.csv',
parse_dates=['date'],
index_col=['date', 'ticker'],
infer_datetime_format=True)
.sort_index())
print(df.info(null_counts=True))
with pd.HDFStore(DATA_STORE) as store:
store.put('quandl/wiki/prices', df)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 15389314 entries, (Timestamp('1962-01-02 00:00:00'), 'ARNC') to (Timestamp('2018-03-27 00:00:00'), 'ZUMZ')
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 15388776 non-null float64
1 high 15389259 non-null float64
2 low 15389259 non-null float64
3 close 15389313 non-null float64
4 volume 15389314 non-null float64
5 ex-dividend 15389314 non-null float64
6 split_ratio 15389313 non-null float64
7 adj_open 15388776 non-null float64
8 adj_high 15389259 non-null float64
9 adj_low 15389259 non-null float64
10 adj_close 15389313 non-null float64
11 adj_volume 15389314 non-null float64
dtypes: float64(12)
memory usage: 1.4+ GB
None
In [45]:
df = pd.read_csv('https://raw.githubusercontent.com/FE-Quant-Study/Machine-Learning-for-Algorithmic-Trading-Second-Edition/master/data/us_equities_meta_data.csv')
df.info()
with pd.HDFStore(DATA_STORE) as store:
store.put('us_equities/stocks', df.set_index('ticker'))
<class 'pandas.core.frame.DataFrame'> RangeIndex: 6834 entries, 0 to 6833 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ticker 6834 non-null object 1 name 6834 non-null object 2 lastsale 6718 non-null float64 3 marketcap 5766 non-null float64 4 ipoyear 3038 non-null float64 5 sector 5288 non-null object 6 industry 5288 non-null object dtypes: float64(3), object(4) memory usage: 373.9+ KB
In [46]:
with pd.HDFStore('./assets.h5') as store:
prices = store['/quandl/wiki/prices'].adj_close.unstack().loc['2010':'2017']
equities = store['/us_equities/stocks'].drop_duplicates()
In [47]:
sectors = equities.filter(prices.columns, axis=0).sector.to_dict()
prices = prices.filter(sectors.keys()).dropna(how='all', axis=1)
In [48]:
returns = prices.resample('M').last().pct_change().mul(100).to_period('M')
returns = returns.dropna(how='all').dropna(axis=1)
returns.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 95 entries, 2010-02 to 2017-12 Freq: M Columns: 1986 entries, A to ZUMZ dtypes: float64(1986) memory usage: 1.4 MB
Align data¶
In [49]:
ff_factor_data = ff_factor_data.loc[returns.index]
ff_portfolio_data = ff_portfolio_data.loc[returns.index]
In [50]:
ff_factor_data.describe()
Out[50]:
| Mkt-RF | SMB | HML | RMW | CMA | RF | |
|---|---|---|---|---|---|---|
| count | 95.00 | 95.00 | 95.00 | 95.00 | 95.00 | 95.00 |
| mean | 1.21 | 0.06 | -0.05 | 0.14 | 0.04 | 0.01 |
| std | 3.57 | 2.31 | 2.21 | 1.58 | 1.42 | 0.02 |
| min | -7.89 | -4.58 | -4.70 | -3.88 | -3.24 | 0.00 |
| 25% | -0.56 | -1.68 | -1.67 | -0.88 | -0.96 | 0.00 |
| 50% | 1.29 | 0.16 | -0.31 | 0.27 | 0.01 | 0.00 |
| 75% | 3.26 | 1.60 | 1.22 | 1.24 | 0.94 | 0.01 |
| max | 11.35 | 7.04 | 8.19 | 3.48 | 3.69 | 0.09 |
Compute excess Returns¶
In [51]:
excess_returns = returns.sub(ff_factor_data.RF, axis=0)
excess_returns.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 95 entries, 2010-02 to 2017-12 Freq: M Columns: 1986 entries, A to ZUMZ dtypes: float64(1986) memory usage: 1.4 MB
In [52]:
excess_returns = excess_returns.clip(lower=np.percentile(excess_returns, 1),
upper=np.percentile(excess_returns, 99))
파마-맥베스 회귀 분석(Fama-Macbeth Regression)¶
리스크 요인 및 포트폴리오 수익에 대한 데이터가 주어지면, 포트폴리오의 노출도를 추정하는 것이 유용합니다. 즉, 얼마나 리스크 요인이 포트폴리오 수익을 설명하는지, 또한 주어진 요인에 대한 노출의 가치는 얼마인지, 즉 시장의 리스크 요인 프리미엄은 얼마인지를 추정하는 것입니다. 그런 다음 리스크 프리미엄을 통해 요인 노출이 알려져 있거나 가정될 경우 어떤 포트폴리오의 수익을 추정할 수 있습니다.
In [53]:
ff_portfolio_data.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 95 entries, 2010-02 to 2017-12 Freq: M Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Food 95 non-null float64 1 Mines 95 non-null float64 2 Oil 95 non-null float64 3 Clths 95 non-null float64 4 Durbl 95 non-null float64 5 Chems 95 non-null float64 6 Cnsum 95 non-null float64 7 Cnstr 95 non-null float64 8 Steel 95 non-null float64 9 FabPr 95 non-null float64 10 Machn 95 non-null float64 11 Cars 95 non-null float64 12 Trans 95 non-null float64 13 Utils 95 non-null float64 14 Rtail 95 non-null float64 15 Finan 95 non-null float64 16 Other 95 non-null float64 dtypes: float64(17) memory usage: 13.4 KB
In [54]:
ff_factor_data = ff_factor_data.drop('RF', axis=1)
ff_factor_data.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 95 entries, 2010-02 to 2017-12 Freq: M Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Mkt-RF 95 non-null float64 1 SMB 95 non-null float64 2 HML 95 non-null float64 3 RMW 95 non-null float64 4 CMA 95 non-null float64 dtypes: float64(5) memory usage: 4.5 KB
상관관계로 인해 발생하는 추론 문제에 대응하기 위해, 파마와 맥베스는 리스크 요인에 대한 수익률의 교차섹션 회귀분석을 위한 두 단계 방법론을 제안했습니다. 두 단계의 파마-맥베스 회귀분석은 시장에서 특정 리스크 요인에 노출된 경우에 보상되는 프리미엄을 추정하는 데 사용됩니다. 두 단계는 다음과 같습니다:
첫 번째 단계: 각 자산 또는 포트폴리오에 대해 N개의 시계열 회귀분석을 수행하여 해당 자산의 초과 수익률을 요인에 대한 로딩 계수를 추정합니다.
두 번째 단계: 각 시간 기간마다 T개의 교차섹션 회귀분석을 수행하여 리스크 프리미엄을 추정합니다.
자세한 내용은 Machine Learning for Trading의 7장에서 해당 섹션을 참조하십시오.
이제 우리는 시간 평균으로 요인 리스크 프리미엄을 계산하고, 그들의 개별적인 유의성을 평가하기 위해 t-통계량을 얻을 수 있습니다. 이 때, 리스크 프리미엄 추정치가 시간을 통해 독립적이라는 가정을 사용합니다.
만약 거래되는 리스크 요인에 대해 매우 크고 대표적인 데이터 샘플이 있다면, 우리는 리스크 프리미엄 추정치로 샘플 평균을 사용할 수 있을 것입니다. 그러나 보통 우리는 충분히 긴 기록을 가지지 않으며, 샘플 평균 주변의 오차 범위가 상당히 크게 될 수 있습니다.
파마-맥베스 방법론은 다른 자산과의 요인간 공분산을 활용하여 요인 프리미엄을 결정합니다. 자산 수익률의 두 번째 모멘트는 첫 번째 모멘트보다 쉽게 추정할 수 있으며, 더 세분화된 데이터를 얻는 것은 추정을 크게 개선시킬 수 있습니다. 이는 평균 추정에는 해당되지 않습니다.
Step 1: Factor Exposures¶
We can implement the first stage to obtain the 17 factor loading estimates as follows:
In [55]:
betas = []
for industry in ff_portfolio_data:
step1 = OLS(endog=ff_portfolio_data.loc[ff_factor_data.index, industry],
exog=add_constant(ff_factor_data)).fit()
betas.append(step1.params.drop('const'))
In [56]:
betas = pd.DataFrame(betas,
columns=ff_factor_data.columns,
index=ff_portfolio_data.columns)
betas.info()
<class 'pandas.core.frame.DataFrame'> Index: 17 entries, Food to Other Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Mkt-RF 17 non-null float64 1 SMB 17 non-null float64 2 HML 17 non-null float64 3 RMW 17 non-null float64 4 CMA 17 non-null float64 dtypes: float64(5) memory usage: 1.3+ KB
Step 2: Risk Premia¶
For the second stage, we run 96 regressions of the period returns for the cross section of portfolios on the factor loadings
In [57]:
lambdas = []
for period in ff_portfolio_data.index:
step2 = OLS(endog=ff_portfolio_data.loc[period, betas.index],
exog=betas).fit()
lambdas.append(step2.params)
In [58]:
lambdas = pd.DataFrame(lambdas,
index=ff_portfolio_data.index,
columns=betas.columns.tolist())
lambdas.info()
<class 'pandas.core.frame.DataFrame'> PeriodIndex: 95 entries, 2010-02 to 2017-12 Freq: M Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Mkt-RF 95 non-null float64 1 SMB 95 non-null float64 2 HML 95 non-null float64 3 RMW 95 non-null float64 4 CMA 95 non-null float64 dtypes: float64(5) memory usage: 6.5 KB
In [59]:
lambdas.mean().sort_values().plot.barh(figsize=(12, 4))
sns.despine()
plt.tight_layout();
In [60]:
t = lambdas.mean().div(lambdas.std())
t
Out[60]:
Mkt-RF 0.34 SMB 0.01 HML -0.27 RMW -0.09 CMA -0.15 dtype: float64
Results¶
In [61]:
window = 24 # months
ax1 = plt.subplot2grid((1, 3), (0, 0))
ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=2)
lambdas.mean().sort_values().plot.barh(ax=ax1)
lambdas.rolling(window).mean().dropna().plot(lw=1,
figsize=(14, 5),
sharey=True,
ax=ax2)
sns.despine()
plt.tight_layout()
In [62]:
window = 24 # months
lambdas.rolling(window).mean().dropna().plot(lw=2,
figsize=(14, 7),
subplots=True,
sharey=True)
sns.despine()
plt.tight_layout()
Fama-Macbeth with the LinearModels library¶
linear_models 라이브러리는 panel 데이터를 위한 다양한 모델을 statsmodels에 추가하며, 또한 두 단계의 파마-맥베스 절차를 구현합니다.
In [63]:
mod = LinearFactorModel(portfolios=ff_portfolio_data,
factors=ff_factor_data)
res = mod.fit()
print(res)
LinearFactorModel Estimation Summary
================================================================================
No. Test Portfolios: 17 R-squared: 0.6879
No. Factors: 5 J-statistic: 15.622
No. Observations: 95 P-value 0.2091
Date: Sat, Jun 24 2023 Distribution: chi2(12)
Time: 16:21:46
Cov. Estimator: robust
Risk Premia Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
Mkt-RF 1.2357 0.4097 3.0158 0.0026 0.4326 2.0388
SMB 0.0202 0.8688 0.0232 0.9815 -1.6828 1.7231
HML -1.1132 0.6218 -1.7905 0.0734 -2.3319 0.1054
RMW -0.2756 0.8131 -0.3389 0.7347 -1.8693 1.3182
CMA -0.5094 0.5661 -0.8998 0.3683 -1.6189 0.6002
==============================================================================
Covariance estimator:
HeteroskedasticCovariance
See full_summary for complete results
In [64]:
print(res.full_summary)
LinearFactorModel Estimation Summary
================================================================================
No. Test Portfolios: 17 R-squared: 0.6879
No. Factors: 5 J-statistic: 15.622
No. Observations: 95 P-value 0.2091
Date: Sat, Jun 24 2023 Distribution: chi2(12)
Time: 16:21:46
Cov. Estimator: robust
Risk Premia Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
Mkt-RF 1.2357 0.4097 3.0158 0.0026 0.4326 2.0388
SMB 0.0202 0.8688 0.0232 0.9815 -1.6828 1.7231
HML -1.1132 0.6218 -1.7905 0.0734 -2.3319 0.1054
RMW -0.2756 0.8131 -0.3389 0.7347 -1.8693 1.3182
CMA -0.5094 0.5661 -0.8998 0.3683 -1.6189 0.6002
Food Coefficients
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
alpha 0.1755 0.2533 0.6927 0.4885 -0.3210 0.6720
Mkt-RF 0.6852 0.0470 14.588 0.0000 0.5931 0.7773
SMB -0.3091 0.1155 -2.6764 0.0074 -0.5354 -0.0827
HML -0.3563 0.1384 -2.5750 0.0100 -0.6275 -0.0851
RMW 0.3074 0.1273 2.4151 0.0157 0.0579 0.5569
CMA 0.4877 0.1596 3.0552 0.0022 0.1748 0.8006
Mines Coefficients
==============================================================================
alpha -0.6975 0.5313 -1.3126 0.1893 -1.7389 0.3440
Mkt-RF 1.3083 0.1957 6.6846 0.0000 0.9247 1.6920
SMB 0.1834 0.3368 0.5445 0.5861 -0.4767 0.8435
HML 0.2224 0.3311 0.6717 0.5018 -0.4265 0.8713
RMW 0.2215 0.4367 0.5072 0.6120 -0.6344 1.0775
CMA 0.5372 0.5502 0.9763 0.3289 -0.5412 1.6156
Oil Coefficients
==============================================================================
alpha 0.2557 0.3490 0.7327 0.4637 -0.4284 0.9398
Mkt-RF 1.0343 0.1007 10.266 0.0000 0.8368 1.2317
SMB 0.1703 0.1894 0.8993 0.3685 -0.2009 0.5416
HML 0.6477 0.2038 3.1775 0.0015 0.2482 1.0473
RMW 0.0033 0.2308 0.0144 0.9885 -0.4490 0.4556
CMA 0.3159 0.2813 1.1228 0.2615 -0.2355 0.8673
Clths Coefficients
==============================================================================
alpha 0.2411 0.3471 0.6946 0.4873 -0.4393 0.9215
Mkt-RF 0.9663 0.1213 7.9686 0.0000 0.7286 1.2040
SMB 0.3610 0.1991 1.8130 0.0698 -0.0293 0.7513
HML -0.1435 0.2122 -0.6764 0.4988 -0.5594 0.2724
RMW 0.6210 0.2716 2.2862 0.0222 0.0886 1.1533
CMA -0.0313 0.3144 -0.0994 0.9208 -0.6474 0.5849
Durbl Coefficients
==============================================================================
alpha -0.0999 0.3215 -0.3108 0.7560 -0.7301 0.5303
Mkt-RF 1.1783 0.0844 13.960 0.0000 1.0129 1.3437
SMB 0.5306 0.1243 4.2697 0.0000 0.2870 0.7741
HML 0.0675 0.1490 0.4533 0.6504 -0.2245 0.3596
RMW 0.5454 0.1993 2.7371 0.0062 0.1549 0.9360
CMA -0.1163 0.2680 -0.4340 0.6643 -0.6415 0.4089
Chems Coefficients
==============================================================================
alpha -0.1740 0.3347 -0.5198 0.6032 -0.8301 0.4821
Mkt-RF 1.3519 0.1065 12.691 0.0000 1.1431 1.5607
SMB 0.1795 0.1479 1.2140 0.2247 -0.1103 0.4693
HML 0.1897 0.1470 1.2908 0.1968 -0.0984 0.4778
RMW 0.1676 0.1927 0.8699 0.3843 -0.2101 0.5453
CMA -0.2408 0.2577 -0.9341 0.3502 -0.7459 0.2644
Cnsum Coefficients
==============================================================================
alpha -0.0647 0.3723 -0.1737 0.8621 -0.7944 0.6651
Mkt-RF 0.7603 0.0579 13.137 0.0000 0.6468 0.8737
SMB -0.3409 0.1004 -3.3964 0.0007 -0.5376 -0.1442
HML -0.5545 0.1191 -4.6569 0.0000 -0.7879 -0.3211
RMW -0.0556 0.1339 -0.4148 0.6783 -0.3180 0.2069
CMA 0.5819 0.2167 2.6849 0.0073 0.1571 1.0067
Cnstr Coefficients
==============================================================================
alpha 0.5788 0.4336 1.3349 0.1819 -0.2710 1.4286
Mkt-RF 1.1147 0.0828 13.468 0.0000 0.9525 1.2769
SMB 0.4288 0.1349 3.1797 0.0015 0.1645 0.6932
HML 0.0903 0.1901 0.4751 0.6347 -0.2823 0.4630
RMW -0.0171 0.2229 -0.0768 0.9388 -0.4539 0.4197
CMA 0.1666 0.2404 0.6932 0.4882 -0.3045 0.6377
Steel Coefficients
==============================================================================
alpha -0.3884 0.4220 -0.9205 0.3573 -1.2155 0.4386
Mkt-RF 1.4689 0.1380 10.644 0.0000 1.1984 1.7394
SMB 0.4216 0.2551 1.6529 0.0983 -0.0783 0.9215
HML 0.4192 0.2618 1.6011 0.1094 -0.0940 0.9324
RMW 0.1969 0.3371 0.5841 0.5591 -0.4638 0.8576
CMA 0.4391 0.4159 1.0560 0.2910 -0.3759 1.2542
FabPr Coefficients
==============================================================================
alpha 0.1708 0.3047 0.5607 0.5750 -0.4263 0.7679
Mkt-RF 1.0680 0.0735 14.523 0.0000 0.9239 1.2121
SMB 0.4642 0.0982 4.7284 0.0000 0.2718 0.6567
HML -0.0379 0.1121 -0.3383 0.7351 -0.2577 0.1818
RMW 0.1639 0.1445 1.1347 0.2565 -0.1192 0.4470
CMA 0.1980 0.1893 1.0459 0.2956 -0.1731 0.5692
Machn Coefficients
==============================================================================
alpha -0.2975 0.2807 -1.0600 0.2891 -0.8476 0.2526
Mkt-RF 1.1883 0.0580 20.502 0.0000 1.0747 1.3020
SMB 0.1775 0.1057 1.6783 0.0933 -0.0298 0.3847
HML 0.0356 0.1045 0.3408 0.7332 -0.1692 0.2404
RMW 0.0586 0.1595 0.3673 0.7134 -0.2540 0.3712
CMA -0.3710 0.1744 -2.1272 0.0334 -0.7129 -0.0292
Cars Coefficients
==============================================================================
alpha -0.1440 0.4019 -0.3583 0.7201 -0.9317 0.6437
Mkt-RF 1.1902 0.0997 11.936 0.0000 0.9948 1.3857
SMB 0.5988 0.1320 4.5352 0.0000 0.3400 0.8576
HML 0.0313 0.1742 0.1796 0.8575 -0.3101 0.3726
RMW 0.0528 0.2195 0.2407 0.8098 -0.3773 0.4830
CMA -0.0100 0.3037 -0.0329 0.9737 -0.6053 0.5853
Trans Coefficients
==============================================================================
alpha 0.4935 0.3259 1.5140 0.1300 -0.1453 1.1323
Mkt-RF 1.0202 0.0500 20.421 0.0000 0.9223 1.1181
SMB 0.2613 0.1043 2.5049 0.0122 0.0568 0.4658
HML 0.0036 0.1217 0.0297 0.9763 -0.2349 0.2421
RMW 0.3908 0.1607 2.4321 0.0150 0.0759 0.7057
CMA 0.2873 0.2003 1.4340 0.1516 -0.1054 0.6800
Utils Coefficients
==============================================================================
alpha 0.3539 0.3066 1.1542 0.2484 -0.2470 0.9548
Mkt-RF 0.5009 0.0904 5.5412 0.0000 0.3237 0.6781
SMB -0.2570 0.1556 -1.6512 0.0987 -0.5620 0.0481
HML -0.2940 0.1777 -1.6542 0.0981 -0.6423 0.0543
RMW 0.2278 0.1933 1.1783 0.2387 -0.1511 0.6066
CMA 0.5477 0.2981 1.8376 0.0661 -0.0365 1.1319
Rtail Coefficients
==============================================================================
alpha 0.0190 0.2715 0.0699 0.9443 -0.5131 0.5510
Mkt-RF 0.9033 0.0697 12.954 0.0000 0.7666 1.0399
SMB 0.1344 0.0998 1.3472 0.1779 -0.0611 0.3299
HML -0.3724 0.1317 -2.8279 0.0047 -0.6306 -0.1143
RMW 0.6896 0.1623 4.2492 0.0000 0.3715 1.0077
CMA 0.1998 0.1755 1.1383 0.2550 -0.1442 0.5439
Finan Coefficients
==============================================================================
alpha 0.4061 0.3806 1.0671 0.2859 -0.3398 1.1521
Mkt-RF 1.0562 0.0429 24.626 0.0000 0.9721 1.1402
SMB 0.0739 0.0893 0.8276 0.4079 -0.1011 0.2489
HML 0.7437 0.0901 8.2589 0.0000 0.5672 0.9202
RMW -0.4517 0.1092 -4.1359 0.0000 -0.6657 -0.2376
CMA -0.5180 0.1157 -4.4787 0.0000 -0.7447 -0.2913
Other Coefficients
==============================================================================
alpha -0.1559 0.2308 -0.6754 0.4994 -0.6082 0.2964
Mkt-RF 1.0389 0.0245 42.353 0.0000 0.9909 1.0870
SMB -0.1183 0.0399 -2.9608 0.0031 -0.1966 -0.0400
HML -0.1918 0.0379 -5.0616 0.0000 -0.2660 -0.1175
RMW -0.0667 0.0630 -1.0573 0.2904 -0.1902 0.0569
CMA 0.0038 0.0652 0.0586 0.9533 -0.1241 0.1317
==============================================================================
Covariance estimator:
HeteroskedasticCovariance
See full_summary for complete results
This provides us with the same result:
In [65]:
lambdas.mean()
Out[65]:
Mkt-RF 1.24 SMB 0.02 HML -1.11 RMW -0.28 CMA -0.51 dtype: float64
수축 방법을 사용한 선형 회귀 규제화¶
과대적합을 방지하는 방법¶
- 규제화 기법: 계수가 큰 값에 도달하지 못하도록 오차 함수에 페널티 항을 추가하는 것
- 선형 모델에 대한 접근 방식을 개선하기 위한 수축 방법
- 예측 정확도
- 해석
리지 회귀 분석의 작동 방식¶
- 리지 회귀는 제곱 계속의 합에 해당하는 페널티를 목표 함수에 추가해 회귀 계수를 수축하는데, 이는 계수 벡터의 L2 규제화에 해당한다.
라쏘 회귀 분석의 작동 방식¶
- 라쏘 회귀는 잔차 제곱의 합에 페널티를 추가해 계수를 수축시키지만 라쏘 페널티는 약간 다른 효과를 가진다. L1 규제화에 해당하는 계수 벡터의 절댓값 합이다.
Preparing Alpha Factors and Features to predict Stock Returns¶
Imports & Settings¶
In [66]:
import warnings
warnings.filterwarnings('ignore')
In [70]:
# colab에서 talib 설치
!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
!tar -xzvf ta-lib-0.4.0-src.tar.gz
%cd ta-lib
!./configure --prefix=/usr
!make
!make install
!pip install Ta-Lib
import talib
--2023-06-24 16:31:48-- http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz Resolving prdownloads.sourceforge.net (prdownloads.sourceforge.net)... 204.68.111.105 Connecting to prdownloads.sourceforge.net (prdownloads.sourceforge.net)|204.68.111.105|:80... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: http://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz [following] --2023-06-24 16:31:48-- http://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz Resolving downloads.sourceforge.net (downloads.sourceforge.net)... 204.68.111.105 Reusing existing connection to prdownloads.sourceforge.net:80. HTTP request sent, awaiting response... 302 Found Location: http://netactuate.dl.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz [following] --2023-06-24 16:31:48-- http://netactuate.dl.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz Resolving netactuate.dl.sourceforge.net (netactuate.dl.sourceforge.net)... 104.225.3.66 Connecting to netactuate.dl.sourceforge.net (netactuate.dl.sourceforge.net)|104.225.3.66|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 1330299 (1.3M) [application/x-gzip] Saving to: ‘ta-lib-0.4.0-src.tar.gz’ ta-lib-0.4.0-src.ta 100%[===================>] 1.27M 6.94MB/s in 0.2s 2023-06-24 16:31:48 (6.94 MB/s) - ‘ta-lib-0.4.0-src.tar.gz’ saved [1330299/1330299] ta-lib/ ta-lib/config.sub ta-lib/aclocal.m4 ta-lib/CHANGELOG.TXT ta-lib/include/ ta-lib/include/ta_abstract.h ta-lib/include/ta_func.h ta-lib/include/ta_common.h ta-lib/include/ta_config.h.in ta-lib/include/Makefile.am ta-lib/include/ta_libc.h ta-lib/include/ta_defs.h ta-lib/missing ta-lib/ta-lib.spec.in ta-lib/config.guess ta-lib/Makefile.in ta-lib/ta-lib.dpkg.in ta-lib/Makefile.am ta-lib/autogen.sh ta-lib/install-sh ta-lib/configure ta-lib/depcomp ta-lib/HISTORY.TXT ta-lib/configure.in ta-lib/autom4te.cache/ ta-lib/autom4te.cache/output.0 ta-lib/autom4te.cache/requests ta-lib/autom4te.cache/output.1 ta-lib/autom4te.cache/traces.0 ta-lib/autom4te.cache/traces.1 ta-lib/ltmain.sh ta-lib/ta-lib-config.in ta-lib/src/ ta-lib/src/ta_func/ ta-lib/src/ta_func/ta_MACDFIX.c ta-lib/src/ta_func/ta_CDLPIERCING.c ta-lib/src/ta_func/ta_DIV.c ta-lib/src/ta_func/ta_ROCR100.c ta-lib/src/ta_func/ta_ADXR.c ta-lib/src/ta_func/ta_MAVP.c ta-lib/src/ta_func/ta_CDLCLOSINGMARUBOZU.c ta-lib/src/ta_func/ta_COSH.c ta-lib/src/ta_func/ta_EXP.c ta-lib/src/ta_func/ta_MINMAXINDEX.c ta-lib/src/ta_func/ta_SQRT.c ta-lib/src/ta_func/ta_FLOOR.c ta-lib/src/ta_func/ta_CDLCONCEALBABYSWALL.c ta-lib/src/ta_func/ta_NATR.c ta-lib/src/ta_func/ta_CDLHARAMICROSS.c ta-lib/src/ta_func/ta_MINUS_DM.c ta-lib/src/ta_func/ta_LOG10.c ta-lib/src/ta_func/ta_LINEARREG_ANGLE.c ta-lib/src/ta_func/ta_RSI.c ta-lib/src/ta_func/ta_CDLABANDONEDBABY.c ta-lib/src/ta_func/ta_SAR.c ta-lib/src/ta_func/ta_CDLBREAKAWAY.c ta-lib/src/ta_func/ta_CDLDRAGONFLYDOJI.c ta-lib/src/ta_func/ta_CDLHIGHWAVE.c ta-lib/src/ta_func/ta_CDLKICKING.c ta-lib/src/ta_func/ta_CDLDOJISTAR.c ta-lib/src/ta_func/ta_VAR.c ta-lib/src/ta_func/ta_CDLMATCHINGLOW.c ta-lib/src/ta_func/ta_CDLGAPSIDESIDEWHITE.c ta-lib/src/ta_func/ta_CDLMARUBOZU.c ta-lib/src/ta_func/ta_AROONOSC.c ta-lib/src/ta_func/ta_WCLPRICE.c ta-lib/src/ta_func/ta_CDLEVENINGDOJISTAR.c ta-lib/src/ta_func/ta_CDL3INSIDE.c ta-lib/src/ta_func/ta_OBV.c ta-lib/src/ta_func/ta_AROON.c ta-lib/src/ta_func/ta_CDLBELTHOLD.c ta-lib/src/ta_func/ta_CDLSPINNINGTOP.c ta-lib/src/ta_func/ta_AD.c ta-lib/src/ta_func/ta_MAX.c ta-lib/src/ta_func/ta_CDLENGULFING.c ta-lib/src/ta_func/ta_MINMAX.c ta-lib/src/ta_func/ta_CDLINNECK.c ta-lib/src/ta_func/ta_STDDEV.c ta-lib/src/ta_func/ta_NVI.c ta-lib/src/ta_func/ta_CDLHAMMER.c ta-lib/src/ta_func/ta_ASIN.c ta-lib/src/ta_func/ta_SUM.c ta-lib/src/ta_func/ta_STOCH.c ta-lib/src/ta_func/ta_CDLLONGLEGGEDDOJI.c ta-lib/src/ta_func/ta_MEDPRICE.c ta-lib/src/ta_func/ta_CDL3STARSINSOUTH.c ta-lib/src/ta_func/ta_HT_TRENDMODE.c ta-lib/src/ta_func/ta_BBANDS.c ta-lib/src/ta_func/ta_CDLMORNINGSTAR.c ta-lib/src/ta_func/ta_HT_DCPHASE.c ta-lib/src/ta_func/ta_CDLLONGLINE.c ta-lib/src/ta_func/ta_TAN.c ta-lib/src/ta_func/ta_SMA.c ta-lib/src/ta_func/ta_DX.c ta-lib/src/ta_func/ta_MIDPOINT.c ta-lib/src/ta_func/ta_CDL2CROWS.c ta-lib/src/ta_func/ta_CORREL.c ta-lib/src/ta_func/ta_CDL3BLACKCROWS.c ta-lib/src/ta_func/ta_ADD.c ta-lib/src/ta_func/Makefile.in ta-lib/src/ta_func/ta_CDLTHRUSTING.c ta-lib/src/ta_func/ta_SUB.c ta-lib/src/ta_func/ta_CDLSTALLEDPATTERN.c ta-lib/src/ta_func/ta_CDLTRISTAR.c ta-lib/src/ta_func/ta_MA.c ta-lib/src/ta_func/ta_HT_SINE.c ta-lib/src/ta_func/ta_ACOS.c ta-lib/src/ta_func/ta_CDLSTICKSANDWICH.c ta-lib/src/ta_func/ta_SINH.c ta-lib/src/ta_func/ta_utility.h ta-lib/src/ta_func/ta_CDLSHORTLINE.c ta-lib/src/ta_func/ta_ATAN.c ta-lib/src/ta_func/ta_CDLADVANCEBLOCK.c ta-lib/src/ta_func/ta_CDLKICKINGBYLENGTH.c ta-lib/src/ta_func/ta_CDLSHOOTINGSTAR.c ta-lib/src/ta_func/ta_ROCR.c ta-lib/src/ta_func/ta_WMA.c ta-lib/src/ta_func/ta_CDLDARKCLOUDCOVER.c ta-lib/src/ta_func/ta_CDLXSIDEGAP3METHODS.c ta-lib/src/ta_func/ta_TYPPRICE.c ta-lib/src/ta_func/ta_CDL3WHITESOLDIERS.c ta-lib/src/ta_func/Makefile.am ta-lib/src/ta_func/ta_MACDEXT.c ta-lib/src/ta_func/ta_ADX.c ta-lib/src/ta_func/ta_PLUS_DM.c ta-lib/src/ta_func/ta_CDLUPSIDEGAP2CROWS.c ta-lib/src/ta_func/ta_LN.c ta-lib/src/ta_func/ta_DEMA.c ta-lib/src/ta_func/ta_CDL3OUTSIDE.c ta-lib/src/ta_func/ta_CDLTASUKIGAP.c ta-lib/src/ta_func/ta_MAMA.c ta-lib/src/ta_func/ta_CDLMORNINGDOJISTAR.c ta-lib/src/ta_func/ta_PLUS_DI.c ta-lib/src/ta_func/ta_MININDEX.c ta-lib/src/ta_func/ta_COS.c ta-lib/src/ta_func/ta_HT_TRENDLINE.c ta-lib/src/ta_func/ta_MIDPRICE.c ta-lib/src/ta_func/ta_CEIL.c ta-lib/src/ta_func/ta_TRIMA.c ta-lib/src/ta_func/ta_CDLSEPARATINGLINES.c ta-lib/src/ta_func/ta_ROCP.c ta-lib/src/ta_func/ta_CDLHOMINGPIGEON.c ta-lib/src/ta_func/ta_CDLHANGINGMAN.c ta-lib/src/ta_func/ta_AVGPRICE.c ta-lib/src/ta_func/ta_APO.c ta-lib/src/ta_func/ta_CDLRISEFALL3METHODS.c ta-lib/src/ta_func/ta_TRANGE.c ta-lib/src/ta_func/ta_TSF.c ta-lib/src/ta_func/ta_LINEARREG.c ta-lib/src/ta_func/ta_PVI.c ta-lib/src/ta_func/ta_CDLHIKKAKEMOD.c ta-lib/src/ta_func/ta_MFI.c ta-lib/src/ta_func/ta_CDLHARAMI.c ta-lib/src/ta_func/ta_MACD.c ta-lib/src/ta_func/ta_BETA.c ta-lib/src/ta_func/ta_CDLINVERTEDHAMMER.c ta-lib/src/ta_func/ta_LINEARREG_SLOPE.c ta-lib/src/ta_func/ta_STOCHF.c ta-lib/src/ta_func/ta_MIN.c ta-lib/src/ta_func/ta_CDLIDENTICAL3CROWS.c ta-lib/src/ta_func/ta_CDLRICKSHAWMAN.c ta-lib/src/ta_func/ta_T3.c ta-lib/src/ta_func/ta_CDLMATHOLD.c ta-lib/src/ta_func/ta_CDLUNIQUE3RIVER.c ta-lib/src/ta_func/ta_ADOSC.c ta-lib/src/ta_func/ta_MAXINDEX.c ta-lib/src/ta_func/ta_ULTOSC.c ta-lib/src/ta_func/ta_TRIX.c ta-lib/src/ta_func/ta_MOM.c ta-lib/src/ta_func/ta_CDLDOJI.c ta-lib/src/ta_func/ta_EMA.c ta-lib/src/ta_func/ta_STOCHRSI.c ta-lib/src/ta_func/ta_ROC.c ta-lib/src/ta_func/ta_CDLEVENINGSTAR.c ta-lib/src/ta_func/ta_CDLCOUNTERATTACK.c ta-lib/src/ta_func/ta_LINEARREG_INTERCEPT.c ta-lib/src/ta_func/ta_SAREXT.c ta-lib/src/ta_func/ta_WILLR.c ta-lib/src/ta_func/ta_MULT.c ta-lib/src/ta_func/ta_ATR.c ta-lib/src/ta_func/ta_BOP.c ta-lib/src/ta_func/ta_CMO.c ta-lib/src/ta_func/ta_CDLONNECK.c ta-lib/src/ta_func/ta_CCI.c ta-lib/src/ta_func/ta_CDLLADDERBOTTOM.c ta-lib/src/ta_func/ta_HT_PHASOR.c ta-lib/src/ta_func/ta_utility.c ta-lib/src/ta_func/ta_PPO.c ta-lib/src/ta_func/ta_CDLHIKKAKE.c ta-lib/src/ta_func/ta_HT_DCPERIOD.c ta-lib/src/ta_func/ta_CDL3LINESTRIKE.c ta-lib/src/ta_func/ta_TEMA.c ta-lib/src/ta_func/ta_SIN.c ta-lib/src/ta_func/ta_MINUS_DI.c ta-lib/src/ta_func/ta_KAMA.c ta-lib/src/ta_func/ta_TANH.c ta-lib/src/ta_func/ta_CDLTAKURI.c ta-lib/src/ta_func/ta_CDLGRAVESTONEDOJI.c ta-lib/src/ta_common/ ta-lib/src/ta_common/ta_pragma.h ta-lib/src/ta_common/ta_magic_nb.h ta-lib/src/ta_common/ta_retcode.csv ta-lib/src/ta_common/Makefile.in ta-lib/src/ta_common/Makefile.am ta-lib/src/ta_common/ta_memory.h ta-lib/src/ta_common/ta_version.c ta-lib/src/ta_common/ta_global.h ta-lib/src/ta_common/ta_global.c ta-lib/src/ta_common/ta_retcode.c ta-lib/src/Makefile.in ta-lib/src/ta_abstract/ ta-lib/src/ta_abstract/frames/ ta-lib/src/ta_abstract/frames/ta_frame.c ta-lib/src/ta_abstract/frames/ta_frame.h ta-lib/src/ta_abstract/excel_glue.c ta-lib/src/ta_abstract/ta_frame_priv.h ta-lib/src/ta_abstract/ta_func_api.c ta-lib/src/ta_abstract/Makefile.in ta-lib/src/ta_abstract/ta_def_ui.h ta-lib/src/ta_abstract/Makefile.am ta-lib/src/ta_abstract/ta_abstract.c ta-lib/src/ta_abstract/ta_group_idx.c ta-lib/src/ta_abstract/tables/ ta-lib/src/ta_abstract/tables/table_u.c ta-lib/src/ta_abstract/tables/table_x.c ta-lib/src/ta_abstract/tables/table_r.c ta-lib/src/ta_abstract/tables/table_f.c ta-lib/src/ta_abstract/tables/table_j.c ta-lib/src/ta_abstract/tables/table_e.c ta-lib/src/ta_abstract/tables/table_t.c ta-lib/src/ta_abstract/tables/table_n.c ta-lib/src/ta_abstract/tables/table_i.c ta-lib/src/ta_abstract/tables/table_c.c ta-lib/src/ta_abstract/tables/table_l.c ta-lib/src/ta_abstract/tables/table_k.c ta-lib/src/ta_abstract/tables/table_g.c ta-lib/src/ta_abstract/tables/table_d.c ta-lib/src/ta_abstract/tables/table_h.c ta-lib/src/ta_abstract/tables/table_o.c ta-lib/src/ta_abstract/tables/table_b.c ta-lib/src/ta_abstract/tables/table_q.c ta-lib/src/ta_abstract/tables/table_v.c ta-lib/src/ta_abstract/tables/table_m.c ta-lib/src/ta_abstract/tables/table_s.c ta-lib/src/ta_abstract/tables/table_y.c ta-lib/src/ta_abstract/tables/table_p.c ta-lib/src/ta_abstract/tables/table_z.c ta-lib/src/ta_abstract/tables/table_a.c ta-lib/src/ta_abstract/tables/table_w.c ta-lib/src/ta_abstract/ta_def_ui.c ta-lib/src/ta_abstract/templates/ ta-lib/src/ta_abstract/templates/ta_x.c.template ta-lib/src/ta_abstract/templates/ta_java_defs.h.template ta-lib/src/ta_abstract/templates/excel_glue.c.template ta-lib/src/ta_abstract/templates/ta_group_idx.c.template ta-lib/src/ta_abstract/templates/ta_frame.c.template ta-lib/src/ta_abstract/templates/CoreAnnotated.java.template ta-lib/src/ta_abstract/templates/ta_func.h.template ta-lib/src/ta_abstract/templates/ta_frame.h.template ta-lib/src/ta_abstract/templates/Makefile.am.template ta-lib/src/ta_abstract/templates/ta_func_api.c.template ta-lib/src/ta_abstract/templates/ta_func.swg.template ta-lib/src/ta_abstract/templates/ta_retcode.c.template ta-lib/src/ta_abstract/ta_java_defs.h ta-lib/src/Makefile.am ta-lib/src/tools/ ta-lib/src/tools/ta_regtest/ ta-lib/src/tools/ta_regtest/test_util.c ta-lib/src/tools/ta_regtest/ta_test_func.h ta-lib/src/tools/ta_regtest/test_data.c ta-lib/src/tools/ta_regtest/ta_gDataHigh.c ta-lib/src/tools/ta_regtest/Makefile.in ta-lib/src/tools/ta_regtest/test_internals.c ta-lib/src/tools/ta_regtest/Makefile.am ta-lib/src/tools/ta_regtest/ta_regtest.c ta-lib/src/tools/ta_regtest/ta_gDataOpen.c ta-lib/src/tools/ta_regtest/ta_gDataClose.c ta-lib/src/tools/ta_regtest/test_abstract.c ta-lib/src/tools/ta_regtest/ta_test_func/ ta-lib/src/tools/ta_regtest/ta_test_func/test_bbands.c ta-lib/src/tools/ta_regtest/ta_test_func/test_stddev.c ta-lib/src/tools/ta_regtest/ta_test_func/test_1in_2out.c ta-lib/src/tools/ta_regtest/ta_test_func/test_sar.c ta-lib/src/tools/ta_regtest/ta_test_func/test_1in_1out.c ta-lib/src/tools/ta_regtest/ta_test_func/test_trange.c ta-lib/src/tools/ta_regtest/ta_test_func/test_macd.c ta-lib/src/tools/ta_regtest/ta_test_func/test_po.c ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hlc.c ta-lib/src/tools/ta_regtest/ta_test_func/test_mom.c ta-lib/src/tools/ta_regtest/ta_test_func/test_per_ohlc.c ta-lib/src/tools/ta_regtest/ta_test_func/test_adx.c ta-lib/src/tools/ta_regtest/ta_test_func/test_candlestick.c ta-lib/src/tools/ta_regtest/ta_test_func/test_rsi.c ta-lib/src/tools/ta_regtest/ta_test_func/test_per_ema.c ta-lib/src/tools/ta_regtest/ta_test_func/test_minmax.c ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hlcv.c ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hl.c ta-lib/src/tools/ta_regtest/ta_test_func/test_stoch.c ta-lib/src/tools/ta_regtest/ta_test_func/test_ma.c 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gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_j.lo -MD -MP -MF .deps/libta_abstract_la-table_j.Tpo -c tables/table_j.c -o libta_abstract_la-table_j.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_j.Tpo .deps/libta_abstract_la-table_j.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_k.lo -MD -MP -MF .deps/libta_abstract_la-table_k.Tpo -c -o libta_abstract_la-table_k.lo `test -f 'tables/table_k.c' || echo './'`tables/table_k.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_k.lo -MD -MP -MF .deps/libta_abstract_la-table_k.Tpo -c tables/table_k.c -fPIC -DPIC -o .libs/libta_abstract_la-table_k.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_k.lo -MD -MP -MF .deps/libta_abstract_la-table_k.Tpo -c tables/table_k.c -o 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/bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_x.lo -MD -MP -MF .deps/libta_abstract_la-table_x.Tpo -c -o libta_abstract_la-table_x.lo `test -f 'tables/table_x.c' || echo './'`tables/table_x.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_x.lo -MD -MP -MF .deps/libta_abstract_la-table_x.Tpo -c tables/table_x.c -fPIC -DPIC -o .libs/libta_abstract_la-table_x.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_x.lo -MD -MP -MF .deps/libta_abstract_la-table_x.Tpo -c tables/table_x.c -o libta_abstract_la-table_x.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_x.Tpo .deps/libta_abstract_la-table_x.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_y.lo -MD -MP -MF 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-I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_z.lo -MD -MP -MF .deps/libta_abstract_la-table_z.Tpo -c tables/table_z.c -fPIC -DPIC -o .libs/libta_abstract_la-table_z.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_z.lo -MD -MP -MF .deps/libta_abstract_la-table_z.Tpo -c tables/table_z.c -o libta_abstract_la-table_z.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_z.Tpo .deps/libta_abstract_la-table_z.Plo /bin/bash ../../libtool --tag=CC --mode=link gcc -g -O2 -version-info 0:0:0 -o libta_abstract.la libta_abstract_la-ta_group_idx.lo libta_abstract_la-ta_def_ui.lo libta_abstract_la-ta_abstract.lo libta_abstract_la-ta_func_api.lo libta_abstract_la-ta_frame.lo libta_abstract_la-table_a.lo libta_abstract_la-table_b.lo libta_abstract_la-table_c.lo libta_abstract_la-table_d.lo libta_abstract_la-table_e.lo libta_abstract_la-table_f.lo libta_abstract_la-table_g.lo libta_abstract_la-table_h.lo libta_abstract_la-table_i.lo libta_abstract_la-table_j.lo libta_abstract_la-table_k.lo libta_abstract_la-table_l.lo libta_abstract_la-table_m.lo libta_abstract_la-table_n.lo libta_abstract_la-table_o.lo libta_abstract_la-table_p.lo libta_abstract_la-table_q.lo libta_abstract_la-table_r.lo libta_abstract_la-table_s.lo libta_abstract_la-table_t.lo libta_abstract_la-table_u.lo libta_abstract_la-table_v.lo libta_abstract_la-table_w.lo libta_abstract_la-table_x.lo libta_abstract_la-table_y.lo libta_abstract_la-table_z.lo -lpthread -ldl libtool: link: warning: `-version-info/-version-number' is ignored for convenience libraries ar cru .libs/libta_abstract.a .libs/libta_abstract_la-ta_group_idx.o .libs/libta_abstract_la-ta_def_ui.o .libs/libta_abstract_la-ta_abstract.o .libs/libta_abstract_la-ta_func_api.o .libs/libta_abstract_la-ta_frame.o .libs/libta_abstract_la-table_a.o .libs/libta_abstract_la-table_b.o .libs/libta_abstract_la-table_c.o .libs/libta_abstract_la-table_d.o .libs/libta_abstract_la-table_e.o .libs/libta_abstract_la-table_f.o .libs/libta_abstract_la-table_g.o .libs/libta_abstract_la-table_h.o .libs/libta_abstract_la-table_i.o .libs/libta_abstract_la-table_j.o .libs/libta_abstract_la-table_k.o .libs/libta_abstract_la-table_l.o .libs/libta_abstract_la-table_m.o .libs/libta_abstract_la-table_n.o .libs/libta_abstract_la-table_o.o .libs/libta_abstract_la-table_p.o .libs/libta_abstract_la-table_q.o .libs/libta_abstract_la-table_r.o .libs/libta_abstract_la-table_s.o .libs/libta_abstract_la-table_t.o .libs/libta_abstract_la-table_u.o .libs/libta_abstract_la-table_v.o .libs/libta_abstract_la-table_w.o .libs/libta_abstract_la-table_x.o .libs/libta_abstract_la-table_y.o .libs/libta_abstract_la-table_z.o ar: `u' modifier ignored since `D' is the default (see `U') ranlib .libs/libta_abstract.a creating libta_abstract.la (cd .libs && rm -f libta_abstract.la && ln -s ../libta_abstract.la libta_abstract.la) /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_group_idx.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_group_idx.Tpo -c -o libta_abstract_gc_la-ta_group_idx.lo `test -f 'ta_group_idx.c' || echo './'`ta_group_idx.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_group_idx.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_group_idx.Tpo -c ta_group_idx.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-ta_group_idx.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_group_idx.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_group_idx.Tpo -c ta_group_idx.c -o libta_abstract_gc_la-ta_group_idx.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-ta_group_idx.Tpo .deps/libta_abstract_gc_la-ta_group_idx.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_def_ui.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_def_ui.Tpo -c -o libta_abstract_gc_la-ta_def_ui.lo `test -f 'ta_def_ui.c' || echo './'`ta_def_ui.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_def_ui.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_def_ui.Tpo -c ta_def_ui.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-ta_def_ui.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_def_ui.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_def_ui.Tpo -c ta_def_ui.c -o libta_abstract_gc_la-ta_def_ui.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-ta_def_ui.Tpo .deps/libta_abstract_gc_la-ta_def_ui.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_abstract.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_abstract.Tpo -c -o libta_abstract_gc_la-ta_abstract.lo `test -f 'ta_abstract.c' || echo './'`ta_abstract.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_abstract.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_abstract.Tpo -c ta_abstract.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-ta_abstract.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_abstract.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_abstract.Tpo -c ta_abstract.c -o libta_abstract_gc_la-ta_abstract.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-ta_abstract.Tpo .deps/libta_abstract_gc_la-ta_abstract.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_func_api.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_func_api.Tpo -c -o libta_abstract_gc_la-ta_func_api.lo `test -f 'ta_func_api.c' || echo './'`ta_func_api.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_func_api.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_func_api.Tpo -c ta_func_api.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-ta_func_api.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_func_api.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_func_api.Tpo -c ta_func_api.c -o libta_abstract_gc_la-ta_func_api.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-ta_func_api.Tpo .deps/libta_abstract_gc_la-ta_func_api.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_frame.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_frame.Tpo -c -o libta_abstract_gc_la-ta_frame.lo `test -f 'frames/ta_frame.c' || echo './'`frames/ta_frame.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_frame.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_frame.Tpo -c frames/ta_frame.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-ta_frame.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-ta_frame.lo -MD -MP -MF .deps/libta_abstract_gc_la-ta_frame.Tpo -c frames/ta_frame.c -o libta_abstract_gc_la-ta_frame.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-ta_frame.Tpo .deps/libta_abstract_gc_la-ta_frame.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_a.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_a.Tpo -c -o libta_abstract_gc_la-table_a.lo `test -f 'tables/table_a.c' || echo './'`tables/table_a.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_a.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_a.Tpo -c tables/table_a.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_a.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_a.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_a.Tpo -c tables/table_a.c -o libta_abstract_gc_la-table_a.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_a.Tpo .deps/libta_abstract_gc_la-table_a.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_b.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_b.Tpo -c -o libta_abstract_gc_la-table_b.lo `test -f 'tables/table_b.c' || echo './'`tables/table_b.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_b.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_b.Tpo -c tables/table_b.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_b.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_b.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_b.Tpo -c tables/table_b.c -o libta_abstract_gc_la-table_b.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_b.Tpo .deps/libta_abstract_gc_la-table_b.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_c.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_c.Tpo -c -o libta_abstract_gc_la-table_c.lo `test -f 'tables/table_c.c' || echo './'`tables/table_c.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_c.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_c.Tpo -c tables/table_c.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_c.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_c.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_c.Tpo -c tables/table_c.c -o libta_abstract_gc_la-table_c.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_c.Tpo .deps/libta_abstract_gc_la-table_c.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_d.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_d.Tpo -c -o libta_abstract_gc_la-table_d.lo `test -f 'tables/table_d.c' || echo './'`tables/table_d.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_d.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_d.Tpo -c tables/table_d.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_d.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_d.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_d.Tpo -c tables/table_d.c -o libta_abstract_gc_la-table_d.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_d.Tpo .deps/libta_abstract_gc_la-table_d.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_e.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_e.Tpo -c -o libta_abstract_gc_la-table_e.lo `test -f 'tables/table_e.c' || echo './'`tables/table_e.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_e.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_e.Tpo -c tables/table_e.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_e.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_e.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_e.Tpo -c tables/table_e.c -o libta_abstract_gc_la-table_e.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_e.Tpo .deps/libta_abstract_gc_la-table_e.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_f.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_f.Tpo -c -o libta_abstract_gc_la-table_f.lo `test -f 'tables/table_f.c' || echo './'`tables/table_f.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_f.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_f.Tpo -c tables/table_f.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_f.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_f.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_f.Tpo -c tables/table_f.c -o libta_abstract_gc_la-table_f.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_f.Tpo .deps/libta_abstract_gc_la-table_f.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_g.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_g.Tpo -c -o libta_abstract_gc_la-table_g.lo `test -f 'tables/table_g.c' || echo './'`tables/table_g.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_g.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_g.Tpo -c tables/table_g.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_g.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_g.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_g.Tpo -c tables/table_g.c -o libta_abstract_gc_la-table_g.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_g.Tpo .deps/libta_abstract_gc_la-table_g.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g 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libta_abstract_gc_la-table_i.lo `test -f 'tables/table_i.c' || echo './'`tables/table_i.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_i.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_i.Tpo -c tables/table_i.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_i.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_i.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_i.Tpo -c tables/table_i.c -o libta_abstract_gc_la-table_i.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_i.Tpo .deps/libta_abstract_gc_la-table_i.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_j.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_j.Tpo -c -o libta_abstract_gc_la-table_j.lo `test -f 'tables/table_j.c' || echo './'`tables/table_j.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_j.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_j.Tpo -c tables/table_j.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_j.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_j.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_j.Tpo -c tables/table_j.c -o libta_abstract_gc_la-table_j.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_j.Tpo .deps/libta_abstract_gc_la-table_j.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_k.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_k.Tpo -c -o libta_abstract_gc_la-table_k.lo `test -f 'tables/table_k.c' || echo './'`tables/table_k.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_k.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_k.Tpo -c tables/table_k.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_k.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_k.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_k.Tpo -c tables/table_k.c -o libta_abstract_gc_la-table_k.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_k.Tpo .deps/libta_abstract_gc_la-table_k.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_l.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_l.Tpo -c -o libta_abstract_gc_la-table_l.lo `test -f 'tables/table_l.c' || echo './'`tables/table_l.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_l.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_l.Tpo -c tables/table_l.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_l.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_l.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_l.Tpo -c tables/table_l.c -o libta_abstract_gc_la-table_l.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_l.Tpo .deps/libta_abstract_gc_la-table_l.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_m.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_m.Tpo -c -o libta_abstract_gc_la-table_m.lo `test -f 'tables/table_m.c' || echo './'`tables/table_m.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_m.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_m.Tpo -c tables/table_m.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_m.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_m.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_m.Tpo -c tables/table_m.c -o libta_abstract_gc_la-table_m.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_m.Tpo .deps/libta_abstract_gc_la-table_m.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_n.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_n.Tpo -c -o libta_abstract_gc_la-table_n.lo `test -f 'tables/table_n.c' || echo './'`tables/table_n.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_n.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_n.Tpo -c tables/table_n.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_n.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_n.lo -MD -MP -MF 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mv -f .deps/libta_abstract_gc_la-table_o.Tpo .deps/libta_abstract_gc_la-table_o.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_p.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_p.Tpo -c -o libta_abstract_gc_la-table_p.lo `test -f 'tables/table_p.c' || echo './'`tables/table_p.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_p.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_p.Tpo -c tables/table_p.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_p.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_p.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_p.Tpo -c tables/table_p.c -o libta_abstract_gc_la-table_p.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_p.Tpo .deps/libta_abstract_gc_la-table_p.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_q.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_q.Tpo -c -o libta_abstract_gc_la-table_q.lo `test -f 'tables/table_q.c' || echo './'`tables/table_q.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_q.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_q.Tpo -c tables/table_q.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_q.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_q.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_q.Tpo -c tables/table_q.c -o libta_abstract_gc_la-table_q.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_q.Tpo .deps/libta_abstract_gc_la-table_q.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g 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libta_abstract_gc_la-table_s.lo `test -f 'tables/table_s.c' || echo './'`tables/table_s.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_s.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_s.Tpo -c tables/table_s.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_s.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_s.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_s.Tpo -c tables/table_s.c -o libta_abstract_gc_la-table_s.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_s.Tpo .deps/libta_abstract_gc_la-table_s.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_t.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_t.Tpo -c -o libta_abstract_gc_la-table_t.lo `test -f 'tables/table_t.c' || echo './'`tables/table_t.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_t.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_t.Tpo -c tables/table_t.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_t.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_t.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_t.Tpo -c tables/table_t.c -o libta_abstract_gc_la-table_t.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_t.Tpo .deps/libta_abstract_gc_la-table_t.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_u.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_u.Tpo -c -o libta_abstract_gc_la-table_u.lo `test -f 'tables/table_u.c' || echo './'`tables/table_u.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT 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-DPIC -o .libs/libta_abstract_gc_la-table_v.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_v.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_v.Tpo -c tables/table_v.c -o libta_abstract_gc_la-table_v.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_v.Tpo .deps/libta_abstract_gc_la-table_v.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_w.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_w.Tpo -c -o libta_abstract_gc_la-table_w.lo `test -f 'tables/table_w.c' || echo './'`tables/table_w.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_w.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_w.Tpo -c tables/table_w.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_w.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_w.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_w.Tpo -c tables/table_w.c -o libta_abstract_gc_la-table_w.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_w.Tpo .deps/libta_abstract_gc_la-table_w.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_x.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_x.Tpo -c -o libta_abstract_gc_la-table_x.lo `test -f 'tables/table_x.c' || echo './'`tables/table_x.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_x.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_x.Tpo -c tables/table_x.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_x.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_x.lo -MD -MP -MF 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mv -f .deps/libta_abstract_gc_la-table_y.Tpo .deps/libta_abstract_gc_la-table_y.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_z.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_z.Tpo -c -o libta_abstract_gc_la-table_z.lo `test -f 'tables/table_z.c' || echo './'`tables/table_z.c gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_z.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_z.Tpo -c tables/table_z.c -fPIC -DPIC -o .libs/libta_abstract_gc_la-table_z.o gcc -DHAVE_CONFIG_H -I. -I../../include -DTA_GEN_CODE -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_gc_la-table_z.lo -MD -MP -MF .deps/libta_abstract_gc_la-table_z.Tpo -c tables/table_z.c -o libta_abstract_gc_la-table_z.o >/dev/null 2>&1 mv -f .deps/libta_abstract_gc_la-table_z.Tpo .deps/libta_abstract_gc_la-table_z.Plo /bin/bash ../../libtool --tag=CC --mode=link gcc -g -O2 -version-info 0:0:0 -o libta_abstract_gc.la libta_abstract_gc_la-ta_group_idx.lo libta_abstract_gc_la-ta_def_ui.lo libta_abstract_gc_la-ta_abstract.lo libta_abstract_gc_la-ta_func_api.lo libta_abstract_gc_la-ta_frame.lo libta_abstract_gc_la-table_a.lo libta_abstract_gc_la-table_b.lo libta_abstract_gc_la-table_c.lo libta_abstract_gc_la-table_d.lo libta_abstract_gc_la-table_e.lo libta_abstract_gc_la-table_f.lo libta_abstract_gc_la-table_g.lo libta_abstract_gc_la-table_h.lo libta_abstract_gc_la-table_i.lo libta_abstract_gc_la-table_j.lo libta_abstract_gc_la-table_k.lo libta_abstract_gc_la-table_l.lo libta_abstract_gc_la-table_m.lo libta_abstract_gc_la-table_n.lo libta_abstract_gc_la-table_o.lo libta_abstract_gc_la-table_p.lo libta_abstract_gc_la-table_q.lo libta_abstract_gc_la-table_r.lo libta_abstract_gc_la-table_s.lo libta_abstract_gc_la-table_t.lo libta_abstract_gc_la-table_u.lo libta_abstract_gc_la-table_v.lo libta_abstract_gc_la-table_w.lo libta_abstract_gc_la-table_x.lo libta_abstract_gc_la-table_y.lo libta_abstract_gc_la-table_z.lo -lpthread -ldl libtool: link: warning: `-version-info/-version-number' is ignored for convenience libraries ar cru .libs/libta_abstract_gc.a .libs/libta_abstract_gc_la-ta_group_idx.o .libs/libta_abstract_gc_la-ta_def_ui.o .libs/libta_abstract_gc_la-ta_abstract.o .libs/libta_abstract_gc_la-ta_func_api.o .libs/libta_abstract_gc_la-ta_frame.o .libs/libta_abstract_gc_la-table_a.o .libs/libta_abstract_gc_la-table_b.o .libs/libta_abstract_gc_la-table_c.o .libs/libta_abstract_gc_la-table_d.o .libs/libta_abstract_gc_la-table_e.o .libs/libta_abstract_gc_la-table_f.o .libs/libta_abstract_gc_la-table_g.o .libs/libta_abstract_gc_la-table_h.o .libs/libta_abstract_gc_la-table_i.o .libs/libta_abstract_gc_la-table_j.o .libs/libta_abstract_gc_la-table_k.o .libs/libta_abstract_gc_la-table_l.o .libs/libta_abstract_gc_la-table_m.o .libs/libta_abstract_gc_la-table_n.o .libs/libta_abstract_gc_la-table_o.o .libs/libta_abstract_gc_la-table_p.o .libs/libta_abstract_gc_la-table_q.o .libs/libta_abstract_gc_la-table_r.o .libs/libta_abstract_gc_la-table_s.o .libs/libta_abstract_gc_la-table_t.o .libs/libta_abstract_gc_la-table_u.o .libs/libta_abstract_gc_la-table_v.o .libs/libta_abstract_gc_la-table_w.o .libs/libta_abstract_gc_la-table_x.o .libs/libta_abstract_gc_la-table_y.o .libs/libta_abstract_gc_la-table_z.o ar: `u' modifier ignored since `D' is the default (see `U') ranlib .libs/libta_abstract_gc.a creating libta_abstract_gc.la (cd .libs && rm -f libta_abstract_gc.la && ln -s ../libta_abstract_gc.la libta_abstract_gc.la) make[2]: Leaving directory '/content/ta-lib/src/ta_abstract' Making all in ta_common make[2]: Entering directory '/content/ta-lib/src/ta_common' /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_global.lo -MD -MP -MF .deps/ta_global.Tpo -c -o ta_global.lo ta_global.c mkdir .libs gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_global.lo -MD -MP -MF .deps/ta_global.Tpo -c ta_global.c -fPIC -DPIC -o .libs/ta_global.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_global.lo -MD -MP -MF .deps/ta_global.Tpo -c ta_global.c -o ta_global.o >/dev/null 2>&1 mv -f .deps/ta_global.Tpo .deps/ta_global.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_retcode.lo -MD -MP -MF .deps/ta_retcode.Tpo -c -o ta_retcode.lo ta_retcode.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_retcode.lo -MD -MP -MF .deps/ta_retcode.Tpo -c ta_retcode.c -fPIC -DPIC -o .libs/ta_retcode.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_retcode.lo -MD -MP -MF .deps/ta_retcode.Tpo -c ta_retcode.c -o ta_retcode.o >/dev/null 2>&1 mv -f .deps/ta_retcode.Tpo .deps/ta_retcode.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_version.lo -MD -MP -MF .deps/ta_version.Tpo -c -o ta_version.lo ta_version.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_version.lo -MD -MP -MF .deps/ta_version.Tpo -c ta_version.c -fPIC -DPIC -o .libs/ta_version.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_version.lo -MD -MP -MF .deps/ta_version.Tpo -c ta_version.c -o ta_version.o >/dev/null 2>&1 mv -f .deps/ta_version.Tpo .deps/ta_version.Plo /bin/bash ../../libtool --tag=CC --mode=link gcc -g -O2 -version-info 0:0:0 -o libta_common.la ta_global.lo ta_retcode.lo ta_version.lo -lpthread -ldl libtool: link: warning: `-version-info/-version-number' is ignored for convenience libraries ar cru .libs/libta_common.a .libs/ta_global.o .libs/ta_retcode.o .libs/ta_version.o ar: `u' modifier ignored since `D' is the default (see `U') ranlib .libs/libta_common.a creating libta_common.la (cd .libs && rm -f libta_common.la && ln -s ../libta_common.la libta_common.la) make[2]: Leaving directory '/content/ta-lib/src/ta_common' Making all in ta_func make[2]: Entering directory '/content/ta-lib/src/ta_func' /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_utility.lo -MD -MP -MF .deps/ta_utility.Tpo -c -o ta_utility.lo ta_utility.c mkdir .libs gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_utility.lo -MD -MP -MF .deps/ta_utility.Tpo -c ta_utility.c -fPIC -DPIC -o .libs/ta_utility.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_utility.lo -MD -MP -MF .deps/ta_utility.Tpo -c ta_utility.c -o ta_utility.o >/dev/null 2>&1 mv -f .deps/ta_utility.Tpo .deps/ta_utility.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ACOS.lo -MD -MP -MF .deps/ta_ACOS.Tpo -c -o ta_ACOS.lo ta_ACOS.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ACOS.lo -MD -MP -MF .deps/ta_ACOS.Tpo -c ta_ACOS.c -fPIC -DPIC -o .libs/ta_ACOS.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ACOS.lo -MD -MP -MF .deps/ta_ACOS.Tpo -c ta_ACOS.c -o ta_ACOS.o >/dev/null 2>&1 mv -f .deps/ta_ACOS.Tpo .deps/ta_ACOS.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AD.lo -MD -MP -MF .deps/ta_AD.Tpo -c -o ta_AD.lo ta_AD.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AD.lo -MD -MP -MF .deps/ta_AD.Tpo -c ta_AD.c -fPIC -DPIC -o .libs/ta_AD.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AD.lo -MD -MP -MF .deps/ta_AD.Tpo -c ta_AD.c -o ta_AD.o >/dev/null 2>&1 mv -f .deps/ta_AD.Tpo .deps/ta_AD.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADD.lo -MD -MP -MF .deps/ta_ADD.Tpo -c -o ta_ADD.lo ta_ADD.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADD.lo -MD -MP -MF .deps/ta_ADD.Tpo -c ta_ADD.c -fPIC -DPIC -o .libs/ta_ADD.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADD.lo -MD -MP -MF .deps/ta_ADD.Tpo -c ta_ADD.c -o ta_ADD.o >/dev/null 2>&1 mv -f .deps/ta_ADD.Tpo .deps/ta_ADD.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADOSC.lo -MD -MP -MF .deps/ta_ADOSC.Tpo -c -o ta_ADOSC.lo ta_ADOSC.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADOSC.lo -MD -MP -MF .deps/ta_ADOSC.Tpo -c ta_ADOSC.c -fPIC -DPIC -o .libs/ta_ADOSC.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADOSC.lo -MD -MP -MF .deps/ta_ADOSC.Tpo -c ta_ADOSC.c -o ta_ADOSC.o >/dev/null 2>&1 mv -f .deps/ta_ADOSC.Tpo .deps/ta_ADOSC.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADX.lo -MD -MP -MF .deps/ta_ADX.Tpo -c -o ta_ADX.lo ta_ADX.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADX.lo -MD -MP -MF .deps/ta_ADX.Tpo -c ta_ADX.c -fPIC -DPIC -o .libs/ta_ADX.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADX.lo -MD -MP -MF .deps/ta_ADX.Tpo -c ta_ADX.c -o ta_ADX.o >/dev/null 2>&1 mv -f .deps/ta_ADX.Tpo .deps/ta_ADX.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADXR.lo -MD -MP -MF .deps/ta_ADXR.Tpo -c -o ta_ADXR.lo ta_ADXR.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADXR.lo -MD -MP -MF .deps/ta_ADXR.Tpo -c ta_ADXR.c -fPIC -DPIC -o .libs/ta_ADXR.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ADXR.lo -MD -MP -MF .deps/ta_ADXR.Tpo -c ta_ADXR.c -o ta_ADXR.o >/dev/null 2>&1 mv -f .deps/ta_ADXR.Tpo .deps/ta_ADXR.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_APO.lo -MD -MP -MF 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-I../../include -I../ta_common/ -g -O2 -MT ta_AROONOSC.lo -MD -MP -MF .deps/ta_AROONOSC.Tpo -c -o ta_AROONOSC.lo ta_AROONOSC.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AROONOSC.lo -MD -MP -MF .deps/ta_AROONOSC.Tpo -c ta_AROONOSC.c -fPIC -DPIC -o .libs/ta_AROONOSC.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AROONOSC.lo -MD -MP -MF .deps/ta_AROONOSC.Tpo -c ta_AROONOSC.c -o ta_AROONOSC.o >/dev/null 2>&1 mv -f .deps/ta_AROONOSC.Tpo .deps/ta_AROONOSC.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ASIN.lo -MD -MP -MF .deps/ta_ASIN.Tpo -c -o ta_ASIN.lo ta_ASIN.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ASIN.lo -MD -MP -MF .deps/ta_ASIN.Tpo -c ta_ASIN.c -fPIC -DPIC -o .libs/ta_ASIN.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ASIN.lo -MD -MP -MF .deps/ta_ASIN.Tpo -c ta_ASIN.c -o ta_ASIN.o >/dev/null 2>&1 mv -f .deps/ta_ASIN.Tpo .deps/ta_ASIN.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATAN.lo -MD -MP -MF .deps/ta_ATAN.Tpo -c -o ta_ATAN.lo ta_ATAN.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATAN.lo -MD -MP -MF .deps/ta_ATAN.Tpo -c ta_ATAN.c -fPIC -DPIC -o .libs/ta_ATAN.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATAN.lo -MD -MP -MF .deps/ta_ATAN.Tpo -c ta_ATAN.c -o ta_ATAN.o >/dev/null 2>&1 mv -f .deps/ta_ATAN.Tpo .deps/ta_ATAN.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATR.lo -MD -MP -MF .deps/ta_ATR.Tpo -c -o ta_ATR.lo ta_ATR.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATR.lo -MD -MP -MF .deps/ta_ATR.Tpo -c ta_ATR.c -fPIC -DPIC -o .libs/ta_ATR.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_ATR.lo -MD -MP -MF .deps/ta_ATR.Tpo -c ta_ATR.c -o ta_ATR.o >/dev/null 2>&1 mv -f .deps/ta_ATR.Tpo .deps/ta_ATR.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AVGPRICE.lo -MD -MP -MF .deps/ta_AVGPRICE.Tpo -c -o ta_AVGPRICE.lo ta_AVGPRICE.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AVGPRICE.lo -MD -MP -MF .deps/ta_AVGPRICE.Tpo -c ta_AVGPRICE.c -fPIC -DPIC -o .libs/ta_AVGPRICE.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_AVGPRICE.lo -MD -MP -MF .deps/ta_AVGPRICE.Tpo -c ta_AVGPRICE.c -o ta_AVGPRICE.o >/dev/null 2>&1 mv -f .deps/ta_AVGPRICE.Tpo .deps/ta_AVGPRICE.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BBANDS.lo -MD -MP -MF .deps/ta_BBANDS.Tpo -c -o ta_BBANDS.lo ta_BBANDS.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BBANDS.lo -MD -MP -MF .deps/ta_BBANDS.Tpo -c ta_BBANDS.c -fPIC -DPIC -o .libs/ta_BBANDS.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BBANDS.lo -MD -MP -MF .deps/ta_BBANDS.Tpo -c ta_BBANDS.c -o ta_BBANDS.o >/dev/null 2>&1 mv -f .deps/ta_BBANDS.Tpo .deps/ta_BBANDS.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BETA.lo -MD -MP -MF .deps/ta_BETA.Tpo -c -o ta_BETA.lo ta_BETA.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BETA.lo -MD -MP -MF .deps/ta_BETA.Tpo -c ta_BETA.c -fPIC -DPIC -o .libs/ta_BETA.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BETA.lo -MD -MP -MF .deps/ta_BETA.Tpo -c ta_BETA.c -o ta_BETA.o >/dev/null 2>&1 mv -f .deps/ta_BETA.Tpo .deps/ta_BETA.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BOP.lo -MD -MP -MF .deps/ta_BOP.Tpo -c -o ta_BOP.lo ta_BOP.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BOP.lo -MD -MP -MF .deps/ta_BOP.Tpo -c ta_BOP.c -fPIC -DPIC -o .libs/ta_BOP.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_BOP.lo -MD -MP -MF .deps/ta_BOP.Tpo -c ta_BOP.c -o ta_BOP.o >/dev/null 2>&1 mv -f .deps/ta_BOP.Tpo .deps/ta_BOP.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_CCI.lo -MD -MP -MF .deps/ta_CCI.Tpo -c -o ta_CCI.lo ta_CCI.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_CCI.lo -MD -MP -MF .deps/ta_CCI.Tpo -c ta_CCI.c -fPIC -DPIC -o .libs/ta_CCI.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_CCI.lo -MD -MP -MF .deps/ta_CCI.Tpo -c ta_CCI.c -o ta_CCI.o >/dev/null 2>&1 mv -f .deps/ta_CCI.Tpo .deps/ta_CCI.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_CDL2CROWS.lo -MD -MP -MF .deps/ta_CDL2CROWS.Tpo -c -o ta_CDL2CROWS.lo ta_CDL2CROWS.c gcc 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ta_WILLR.lo -MD -MP -MF .deps/ta_WILLR.Tpo -c -o ta_WILLR.lo ta_WILLR.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WILLR.lo -MD -MP -MF .deps/ta_WILLR.Tpo -c ta_WILLR.c -fPIC -DPIC -o .libs/ta_WILLR.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WILLR.lo -MD -MP -MF .deps/ta_WILLR.Tpo -c ta_WILLR.c -o ta_WILLR.o >/dev/null 2>&1 mv -f .deps/ta_WILLR.Tpo .deps/ta_WILLR.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WMA.lo -MD -MP -MF .deps/ta_WMA.Tpo -c -o ta_WMA.lo ta_WMA.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WMA.lo -MD -MP -MF .deps/ta_WMA.Tpo -c ta_WMA.c -fPIC -DPIC -o .libs/ta_WMA.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WMA.lo -MD -MP -MF .deps/ta_WMA.Tpo -c ta_WMA.c -o ta_WMA.o >/dev/null 2>&1 mv -f .deps/ta_WMA.Tpo .deps/ta_WMA.Plo /bin/bash ../../libtool --tag=CC --mode=link gcc -g -O2 -version-info 0:0:0 -o libta_func.la ta_utility.lo ta_ACOS.lo ta_AD.lo ta_ADD.lo ta_ADOSC.lo ta_ADX.lo ta_ADXR.lo ta_APO.lo ta_AROON.lo ta_AROONOSC.lo ta_ASIN.lo ta_ATAN.lo ta_ATR.lo ta_AVGPRICE.lo ta_BBANDS.lo ta_BETA.lo ta_BOP.lo ta_CCI.lo ta_CDL2CROWS.lo ta_CDL3BLACKCROWS.lo ta_CDL3INSIDE.lo ta_CDL3LINESTRIKE.lo ta_CDL3OUTSIDE.lo ta_CDL3STARSINSOUTH.lo ta_CDL3WHITESOLDIERS.lo ta_CDLABANDONEDBABY.lo ta_CDLADVANCEBLOCK.lo ta_CDLBELTHOLD.lo ta_CDLBREAKAWAY.lo ta_CDLCLOSINGMARUBOZU.lo ta_CDLCONCEALBABYSWALL.lo ta_CDLCOUNTERATTACK.lo ta_CDLDARKCLOUDCOVER.lo ta_CDLDOJI.lo ta_CDLDOJISTAR.lo ta_CDLDRAGONFLYDOJI.lo ta_CDLENGULFING.lo ta_CDLEVENINGDOJISTAR.lo ta_CDLEVENINGSTAR.lo ta_CDLGAPSIDESIDEWHITE.lo ta_CDLGRAVESTONEDOJI.lo ta_CDLHAMMER.lo ta_CDLHANGINGMAN.lo ta_CDLHARAMI.lo ta_CDLHARAMICROSS.lo ta_CDLHIGHWAVE.lo ta_CDLHIKKAKE.lo ta_CDLHIKKAKEMOD.lo ta_CDLHOMINGPIGEON.lo ta_CDLIDENTICAL3CROWS.lo ta_CDLINNECK.lo ta_CDLINVERTEDHAMMER.lo ta_CDLKICKING.lo ta_CDLKICKINGBYLENGTH.lo ta_CDLLADDERBOTTOM.lo ta_CDLLONGLEGGEDDOJI.lo ta_CDLLONGLINE.lo ta_CDLMARUBOZU.lo ta_CDLMATCHINGLOW.lo ta_CDLMATHOLD.lo ta_CDLMORNINGDOJISTAR.lo ta_CDLMORNINGSTAR.lo ta_CDLONNECK.lo ta_CDLPIERCING.lo ta_CDLRICKSHAWMAN.lo ta_CDLRISEFALL3METHODS.lo ta_CDLSEPARATINGLINES.lo ta_CDLSHOOTINGSTAR.lo ta_CDLSHORTLINE.lo ta_CDLSPINNINGTOP.lo ta_CDLSTALLEDPATTERN.lo ta_CDLSTICKSANDWICH.lo ta_CDLTAKURI.lo ta_CDLTASUKIGAP.lo ta_CDLTHRUSTING.lo ta_CDLTRISTAR.lo ta_CDLUNIQUE3RIVER.lo ta_CDLUPSIDEGAP2CROWS.lo ta_CDLXSIDEGAP3METHODS.lo ta_CEIL.lo ta_CMO.lo ta_CORREL.lo ta_COS.lo ta_COSH.lo ta_DEMA.lo ta_DIV.lo ta_DX.lo ta_EMA.lo ta_EXP.lo ta_FLOOR.lo ta_HT_DCPERIOD.lo ta_HT_DCPHASE.lo ta_HT_PHASOR.lo ta_HT_SINE.lo ta_HT_TRENDLINE.lo ta_HT_TRENDMODE.lo ta_KAMA.lo ta_LINEARREG.lo ta_LINEARREG_ANGLE.lo ta_LINEARREG_INTERCEPT.lo ta_LINEARREG_SLOPE.lo ta_LN.lo ta_LOG10.lo ta_MA.lo ta_MACD.lo ta_MACDEXT.lo ta_MACDFIX.lo ta_MAMA.lo ta_MAVP.lo ta_MAX.lo ta_MAXINDEX.lo ta_MEDPRICE.lo ta_MFI.lo ta_MIDPOINT.lo ta_MIDPRICE.lo ta_MIN.lo ta_MININDEX.lo ta_MINMAX.lo ta_MINMAXINDEX.lo ta_MINUS_DI.lo ta_MINUS_DM.lo ta_MOM.lo ta_MULT.lo ta_NATR.lo ta_OBV.lo ta_PLUS_DI.lo ta_PLUS_DM.lo ta_PPO.lo ta_ROC.lo ta_ROCP.lo ta_ROCR.lo ta_ROCR100.lo ta_RSI.lo ta_SAR.lo ta_SAREXT.lo ta_SIN.lo ta_SINH.lo ta_SMA.lo ta_SQRT.lo ta_STDDEV.lo ta_STOCH.lo ta_STOCHF.lo ta_STOCHRSI.lo ta_SUB.lo ta_SUM.lo ta_T3.lo ta_TAN.lo ta_TANH.lo ta_TEMA.lo ta_TRANGE.lo ta_TRIMA.lo ta_TRIX.lo ta_TSF.lo ta_TYPPRICE.lo ta_ULTOSC.lo ta_VAR.lo ta_WCLPRICE.lo ta_WILLR.lo ta_WMA.lo -lpthread -ldl libtool: link: warning: `-version-info/-version-number' is ignored for convenience libraries ar cru .libs/libta_func.a .libs/ta_utility.o .libs/ta_ACOS.o .libs/ta_AD.o .libs/ta_ADD.o .libs/ta_ADOSC.o .libs/ta_ADX.o .libs/ta_ADXR.o .libs/ta_APO.o .libs/ta_AROON.o .libs/ta_AROONOSC.o .libs/ta_ASIN.o .libs/ta_ATAN.o .libs/ta_ATR.o .libs/ta_AVGPRICE.o .libs/ta_BBANDS.o .libs/ta_BETA.o .libs/ta_BOP.o .libs/ta_CCI.o .libs/ta_CDL2CROWS.o .libs/ta_CDL3BLACKCROWS.o .libs/ta_CDL3INSIDE.o .libs/ta_CDL3LINESTRIKE.o .libs/ta_CDL3OUTSIDE.o .libs/ta_CDL3STARSINSOUTH.o .libs/ta_CDL3WHITESOLDIERS.o .libs/ta_CDLABANDONEDBABY.o .libs/ta_CDLADVANCEBLOCK.o .libs/ta_CDLBELTHOLD.o .libs/ta_CDLBREAKAWAY.o .libs/ta_CDLCLOSINGMARUBOZU.o .libs/ta_CDLCONCEALBABYSWALL.o .libs/ta_CDLCOUNTERATTACK.o .libs/ta_CDLDARKCLOUDCOVER.o .libs/ta_CDLDOJI.o .libs/ta_CDLDOJISTAR.o .libs/ta_CDLDRAGONFLYDOJI.o .libs/ta_CDLENGULFING.o .libs/ta_CDLEVENINGDOJISTAR.o .libs/ta_CDLEVENINGSTAR.o .libs/ta_CDLGAPSIDESIDEWHITE.o .libs/ta_CDLGRAVESTONEDOJI.o .libs/ta_CDLHAMMER.o .libs/ta_CDLHANGINGMAN.o .libs/ta_CDLHARAMI.o .libs/ta_CDLHARAMICROSS.o .libs/ta_CDLHIGHWAVE.o .libs/ta_CDLHIKKAKE.o .libs/ta_CDLHIKKAKEMOD.o .libs/ta_CDLHOMINGPIGEON.o .libs/ta_CDLIDENTICAL3CROWS.o .libs/ta_CDLINNECK.o .libs/ta_CDLINVERTEDHAMMER.o .libs/ta_CDLKICKING.o .libs/ta_CDLKICKINGBYLENGTH.o .libs/ta_CDLLADDERBOTTOM.o .libs/ta_CDLLONGLEGGEDDOJI.o .libs/ta_CDLLONGLINE.o .libs/ta_CDLMARUBOZU.o .libs/ta_CDLMATCHINGLOW.o .libs/ta_CDLMATHOLD.o .libs/ta_CDLMORNINGDOJISTAR.o .libs/ta_CDLMORNINGSTAR.o .libs/ta_CDLONNECK.o .libs/ta_CDLPIERCING.o .libs/ta_CDLRICKSHAWMAN.o .libs/ta_CDLRISEFALL3METHODS.o .libs/ta_CDLSEPARATINGLINES.o .libs/ta_CDLSHOOTINGSTAR.o .libs/ta_CDLSHORTLINE.o .libs/ta_CDLSPINNINGTOP.o .libs/ta_CDLSTALLEDPATTERN.o .libs/ta_CDLSTICKSANDWICH.o .libs/ta_CDLTAKURI.o .libs/ta_CDLTASUKIGAP.o .libs/ta_CDLTHRUSTING.o .libs/ta_CDLTRISTAR.o .libs/ta_CDLUNIQUE3RIVER.o .libs/ta_CDLUPSIDEGAP2CROWS.o .libs/ta_CDLXSIDEGAP3METHODS.o .libs/ta_CEIL.o .libs/ta_CMO.o .libs/ta_CORREL.o .libs/ta_COS.o .libs/ta_COSH.o .libs/ta_DEMA.o .libs/ta_DIV.o .libs/ta_DX.o .libs/ta_EMA.o .libs/ta_EXP.o .libs/ta_FLOOR.o .libs/ta_HT_DCPERIOD.o .libs/ta_HT_DCPHASE.o .libs/ta_HT_PHASOR.o .libs/ta_HT_SINE.o .libs/ta_HT_TRENDLINE.o .libs/ta_HT_TRENDMODE.o .libs/ta_KAMA.o .libs/ta_LINEARREG.o .libs/ta_LINEARREG_ANGLE.o .libs/ta_LINEARREG_INTERCEPT.o .libs/ta_LINEARREG_SLOPE.o .libs/ta_LN.o .libs/ta_LOG10.o .libs/ta_MA.o .libs/ta_MACD.o .libs/ta_MACDEXT.o .libs/ta_MACDFIX.o .libs/ta_MAMA.o .libs/ta_MAVP.o .libs/ta_MAX.o .libs/ta_MAXINDEX.o .libs/ta_MEDPRICE.o .libs/ta_MFI.o .libs/ta_MIDPOINT.o .libs/ta_MIDPRICE.o .libs/ta_MIN.o .libs/ta_MININDEX.o .libs/ta_MINMAX.o .libs/ta_MINMAXINDEX.o .libs/ta_MINUS_DI.o .libs/ta_MINUS_DM.o .libs/ta_MOM.o .libs/ta_MULT.o .libs/ta_NATR.o .libs/ta_OBV.o .libs/ta_PLUS_DI.o .libs/ta_PLUS_DM.o .libs/ta_PPO.o .libs/ta_ROC.o .libs/ta_ROCP.o .libs/ta_ROCR.o .libs/ta_ROCR100.o .libs/ta_RSI.o .libs/ta_SAR.o .libs/ta_SAREXT.o .libs/ta_SIN.o .libs/ta_SINH.o .libs/ta_SMA.o .libs/ta_SQRT.o .libs/ta_STDDEV.o .libs/ta_STOCH.o .libs/ta_STOCHF.o .libs/ta_STOCHRSI.o .libs/ta_SUB.o .libs/ta_SUM.o .libs/ta_T3.o .libs/ta_TAN.o .libs/ta_TANH.o .libs/ta_TEMA.o .libs/ta_TRANGE.o .libs/ta_TRIMA.o .libs/ta_TRIX.o .libs/ta_TSF.o .libs/ta_TYPPRICE.o .libs/ta_ULTOSC.o .libs/ta_VAR.o .libs/ta_WCLPRICE.o .libs/ta_WILLR.o .libs/ta_WMA.o ar: `u' modifier ignored since `D' is the default (see `U') ranlib .libs/libta_func.a creating libta_func.la (cd .libs && rm -f libta_func.la && ln -s ../libta_func.la libta_func.la) make[2]: Leaving directory '/content/ta-lib/src/ta_func' make[2]: Entering directory '/content/ta-lib/src' /bin/bash ../libtool --tag=CC --mode=link gcc -g -O2 -version-info 0:0:0 -o libta_lib.la -rpath /usr/lib ta_abstract/libta_abstract.la ta_func/libta_func.la ta_common/libta_common.la -lpthread -ldl mkdir .libs gcc -shared -Wl,--whole-archive ta_abstract/.libs/libta_abstract.a ta_func/.libs/libta_func.a ta_common/.libs/libta_common.a -Wl,--no-whole-archive -lpthread -ldl -Wl,-soname -Wl,libta_lib.so.0 -o .libs/libta_lib.so.0.0.0 (cd .libs && rm -f libta_lib.so.0 && ln -s libta_lib.so.0.0.0 libta_lib.so.0) (cd .libs && rm -f libta_lib.so && ln -s libta_lib.so.0.0.0 libta_lib.so) rm -fr .libs/libta_lib.lax mkdir .libs/libta_lib.lax rm -fr .libs/libta_lib.lax/libta_abstract.a mkdir .libs/libta_lib.lax/libta_abstract.a (cd .libs/libta_lib.lax/libta_abstract.a && ar x /content/ta-lib/src/ta_abstract/.libs/libta_abstract.a) rm -fr .libs/libta_lib.lax/libta_func.a mkdir .libs/libta_lib.lax/libta_func.a (cd .libs/libta_lib.lax/libta_func.a && ar x /content/ta-lib/src/ta_func/.libs/libta_func.a) rm -fr .libs/libta_lib.lax/libta_common.a mkdir .libs/libta_lib.lax/libta_common.a (cd .libs/libta_lib.lax/libta_common.a && ar x /content/ta-lib/src/ta_common/.libs/libta_common.a) ar cru .libs/libta_lib.a .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_n.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_q.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_c.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-ta_group_idx.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_o.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-ta_func_api.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-ta_frame.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_d.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_r.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_e.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_k.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_l.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_m.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_g.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_s.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_v.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_z.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_j.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-ta_abstract.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_f.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_w.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_a.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_h.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-ta_def_ui.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_y.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_b.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_x.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_u.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_p.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_i.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_t.o .libs/libta_lib.lax/libta_func.a/ta_MAXINDEX.o .libs/libta_lib.lax/libta_func.a/ta_PLUS_DI.o .libs/libta_lib.lax/libta_func.a/ta_SIN.o .libs/libta_lib.lax/libta_func.a/ta_LN.o .libs/libta_lib.lax/libta_func.a/ta_CDL3LINESTRIKE.o .libs/libta_lib.lax/libta_func.a/ta_DEMA.o .libs/libta_lib.lax/libta_func.a/ta_HT_TRENDLINE.o .libs/libta_lib.lax/libta_func.a/ta_CDLSHORTLINE.o .libs/libta_lib.lax/libta_func.a/ta_CDLINVERTEDHAMMER.o .libs/libta_lib.lax/libta_func.a/ta_OBV.o .libs/libta_lib.lax/libta_func.a/ta_CDLLONGLEGGEDDOJI.o .libs/libta_lib.lax/libta_func.a/ta_KAMA.o .libs/libta_lib.lax/libta_func.a/ta_CDLKICKING.o .libs/libta_lib.lax/libta_func.a/ta_TRIMA.o .libs/libta_lib.lax/libta_func.a/ta_BOP.o .libs/libta_lib.lax/libta_func.a/ta_CDLMORNINGDOJISTAR.o .libs/libta_lib.lax/libta_func.a/ta_AD.o .libs/libta_lib.lax/libta_func.a/ta_CDLMATCHINGLOW.o .libs/libta_lib.lax/libta_func.a/ta_AROONOSC.o .libs/libta_lib.lax/libta_func.a/ta_CDLDARKCLOUDCOVER.o .libs/libta_lib.lax/libta_func.a/ta_RSI.o .libs/libta_lib.lax/libta_func.a/ta_HT_TRENDMODE.o .libs/libta_lib.lax/libta_func.a/ta_CDLHIKKAKE.o .libs/libta_lib.lax/libta_func.a/ta_CDLHIKKAKEMOD.o .libs/libta_lib.lax/libta_func.a/ta_CDL3OUTSIDE.o .libs/libta_lib.lax/libta_func.a/ta_STOCH.o .libs/libta_lib.lax/libta_func.a/ta_STOCHRSI.o .libs/libta_lib.lax/libta_func.a/ta_MININDEX.o .libs/libta_lib.lax/libta_func.a/ta_MAMA.o .libs/libta_lib.lax/libta_func.a/ta_CDLADVANCEBLOCK.o .libs/libta_lib.lax/libta_func.a/ta_CDLHIGHWAVE.o .libs/libta_lib.lax/libta_func.a/ta_CDLPIERCING.o .libs/libta_lib.lax/libta_func.a/ta_WMA.o .libs/libta_lib.lax/libta_func.a/ta_MINMAX.o .libs/libta_lib.lax/libta_func.a/ta_BBANDS.o .libs/libta_lib.lax/libta_func.a/ta_CDLSPINNINGTOP.o .libs/libta_lib.lax/libta_func.a/ta_CEIL.o .libs/libta_lib.lax/libta_func.a/ta_CORREL.o .libs/libta_lib.lax/libta_func.a/ta_ROCR100.o .libs/libta_lib.lax/libta_func.a/ta_CDLHARAMICROSS.o .libs/libta_lib.lax/libta_func.a/ta_ROCP.o .libs/libta_lib.lax/libta_func.a/ta_ACOS.o .libs/libta_lib.lax/libta_func.a/ta_CDLABANDONEDBABY.o .libs/libta_lib.lax/libta_func.a/ta_MOM.o .libs/libta_lib.lax/libta_func.a/ta_CDLLONGLINE.o .libs/libta_lib.lax/libta_func.a/ta_TANH.o .libs/libta_lib.lax/libta_func.a/ta_CDLHARAMI.o .libs/libta_lib.lax/libta_func.a/ta_MINUS_DM.o .libs/libta_lib.lax/libta_func.a/ta_SQRT.o .libs/libta_lib.lax/libta_func.a/ta_CDLINNECK.o .libs/libta_lib.lax/libta_func.a/ta_CDLSTALLEDPATTERN.o .libs/libta_lib.lax/libta_func.a/ta_CDLBREAKAWAY.o .libs/libta_lib.lax/libta_func.a/ta_WCLPRICE.o .libs/libta_lib.lax/libta_func.a/ta_SUB.o .libs/libta_lib.lax/libta_func.a/ta_TRANGE.o .libs/libta_lib.lax/libta_func.a/ta_CDLTAKURI.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG.o .libs/libta_lib.lax/libta_func.a/ta_CDLCOUNTERATTACK.o .libs/libta_lib.lax/libta_func.a/ta_CDLCONCEALBABYSWALL.o .libs/libta_lib.lax/libta_func.a/ta_CDL3WHITESOLDIERS.o .libs/libta_lib.lax/libta_func.a/ta_MACDEXT.o .libs/libta_lib.lax/libta_func.a/ta_TRIX.o .libs/libta_lib.lax/libta_func.a/ta_SUM.o .libs/libta_lib.lax/libta_func.a/ta_CDLDRAGONFLYDOJI.o .libs/libta_lib.lax/libta_func.a/ta_MACD.o .libs/libta_lib.lax/libta_func.a/ta_CDLEVENINGDOJISTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLCLOSINGMARUBOZU.o .libs/libta_lib.lax/libta_func.a/ta_CMO.o .libs/libta_lib.lax/libta_func.a/ta_CDL3BLACKCROWS.o .libs/libta_lib.lax/libta_func.a/ta_EMA.o .libs/libta_lib.lax/libta_func.a/ta_MIN.o .libs/libta_lib.lax/libta_func.a/ta_ADOSC.o .libs/libta_lib.lax/libta_func.a/ta_HT_DCPHASE.o .libs/libta_lib.lax/libta_func.a/ta_SMA.o .libs/libta_lib.lax/libta_func.a/ta_CDL3STARSINSOUTH.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG_SLOPE.o .libs/libta_lib.lax/libta_func.a/ta_ADXR.o .libs/libta_lib.lax/libta_func.a/ta_COSH.o .libs/libta_lib.lax/libta_func.a/ta_CDLXSIDEGAP3METHODS.o .libs/libta_lib.lax/libta_func.a/ta_CDLONNECK.o .libs/libta_lib.lax/libta_func.a/ta_CDLEVENINGSTAR.o .libs/libta_lib.lax/libta_func.a/ta_T3.o .libs/libta_lib.lax/libta_func.a/ta_TYPPRICE.o .libs/libta_lib.lax/libta_func.a/ta_CDLSEPARATINGLINES.o .libs/libta_lib.lax/libta_func.a/ta_CDLHAMMER.o .libs/libta_lib.lax/libta_func.a/ta_CDLIDENTICAL3CROWS.o .libs/libta_lib.lax/libta_func.a/ta_CDLUPSIDEGAP2CROWS.o .libs/libta_lib.lax/libta_func.a/ta_TSF.o .libs/libta_lib.lax/libta_func.a/ta_MIDPOINT.o .libs/libta_lib.lax/libta_func.a/ta_MA.o .libs/libta_lib.lax/libta_func.a/ta_HT_PHASOR.o .libs/libta_lib.lax/libta_func.a/ta_NATR.o .libs/libta_lib.lax/libta_func.a/ta_ATAN.o .libs/libta_lib.lax/libta_func.a/ta_CDLENGULFING.o .libs/libta_lib.lax/libta_func.a/ta_ROCR.o .libs/libta_lib.lax/libta_func.a/ta_MINUS_DI.o .libs/libta_lib.lax/libta_func.a/ta_MFI.o .libs/libta_lib.lax/libta_func.a/ta_CDLTASUKIGAP.o .libs/libta_lib.lax/libta_func.a/ta_CDL2CROWS.o .libs/libta_lib.lax/libta_func.a/ta_CDLSHOOTINGSTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLDOJI.o .libs/libta_lib.lax/libta_func.a/ta_ASIN.o .libs/libta_lib.lax/libta_func.a/ta_CDLBELTHOLD.o .libs/libta_lib.lax/libta_func.a/ta_EXP.o .libs/libta_lib.lax/libta_func.a/ta_CDLKICKINGBYLENGTH.o .libs/libta_lib.lax/libta_func.a/ta_MAX.o .libs/libta_lib.lax/libta_func.a/ta_MULT.o .libs/libta_lib.lax/libta_func.a/ta_ATR.o .libs/libta_lib.lax/libta_func.a/ta_CDLDOJISTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLGRAVESTONEDOJI.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG_INTERCEPT.o .libs/libta_lib.lax/libta_func.a/ta_SAR.o .libs/libta_lib.lax/libta_func.a/ta_MACDFIX.o .libs/libta_lib.lax/libta_func.a/ta_MINMAXINDEX.o .libs/libta_lib.lax/libta_func.a/ta_ULTOSC.o .libs/libta_lib.lax/libta_func.a/ta_ROC.o .libs/libta_lib.lax/libta_func.a/ta_CCI.o .libs/libta_lib.lax/libta_func.a/ta_CDLRISEFALL3METHODS.o .libs/libta_lib.lax/libta_func.a/ta_STDDEV.o .libs/libta_lib.lax/libta_func.a/ta_CDLLADDERBOTTOM.o .libs/libta_lib.lax/libta_func.a/ta_COS.o .libs/libta_lib.lax/libta_func.a/ta_TAN.o .libs/libta_lib.lax/libta_func.a/ta_SAREXT.o .libs/libta_lib.lax/libta_func.a/ta_HT_SINE.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG_ANGLE.o .libs/libta_lib.lax/libta_func.a/ta_MEDPRICE.o .libs/libta_lib.lax/libta_func.a/ta_ADX.o .libs/libta_lib.lax/libta_func.a/ta_CDLGAPSIDESIDEWHITE.o .libs/libta_lib.lax/libta_func.a/ta_APO.o .libs/libta_lib.lax/libta_func.a/ta_MAVP.o .libs/libta_lib.lax/libta_func.a/ta_PLUS_DM.o .libs/libta_lib.lax/libta_func.a/ta_BETA.o .libs/libta_lib.lax/libta_func.a/ta_CDLRICKSHAWMAN.o .libs/libta_lib.lax/libta_func.a/ta_TEMA.o .libs/libta_lib.lax/libta_func.a/ta_ADD.o .libs/libta_lib.lax/libta_func.a/ta_SINH.o .libs/libta_lib.lax/libta_func.a/ta_STOCHF.o .libs/libta_lib.lax/libta_func.a/ta_CDLHANGINGMAN.o .libs/libta_lib.lax/libta_func.a/ta_FLOOR.o .libs/libta_lib.lax/libta_func.a/ta_CDLSTICKSANDWICH.o .libs/libta_lib.lax/libta_func.a/ta_utility.o .libs/libta_lib.lax/libta_func.a/ta_CDLTHRUSTING.o .libs/libta_lib.lax/libta_func.a/ta_VAR.o .libs/libta_lib.lax/libta_func.a/ta_AROON.o .libs/libta_lib.lax/libta_func.a/ta_WILLR.o .libs/libta_lib.lax/libta_func.a/ta_DIV.o .libs/libta_lib.lax/libta_func.a/ta_CDLHOMINGPIGEON.o .libs/libta_lib.lax/libta_func.a/ta_HT_DCPERIOD.o .libs/libta_lib.lax/libta_func.a/ta_LOG10.o .libs/libta_lib.lax/libta_func.a/ta_CDLMATHOLD.o .libs/libta_lib.lax/libta_func.a/ta_DX.o .libs/libta_lib.lax/libta_func.a/ta_AVGPRICE.o .libs/libta_lib.lax/libta_func.a/ta_CDL3INSIDE.o .libs/libta_lib.lax/libta_func.a/ta_CDLTRISTAR.o .libs/libta_lib.lax/libta_func.a/ta_MIDPRICE.o .libs/libta_lib.lax/libta_func.a/ta_PPO.o .libs/libta_lib.lax/libta_func.a/ta_CDLMARUBOZU.o .libs/libta_lib.lax/libta_func.a/ta_CDLMORNINGSTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLUNIQUE3RIVER.o .libs/libta_lib.lax/libta_common.a/ta_global.o .libs/libta_lib.lax/libta_common.a/ta_version.o .libs/libta_lib.lax/libta_common.a/ta_retcode.o ar: `u' modifier ignored since `D' is the default (see `U') ranlib .libs/libta_lib.a rm -fr .libs/libta_lib.lax creating libta_lib.la (cd .libs && rm -f libta_lib.la && ln -s ../libta_lib.la libta_lib.la) make[2]: Leaving directory '/content/ta-lib/src' make[1]: Leaving directory '/content/ta-lib/src' Making all in src/tools make[1]: Entering directory '/content/ta-lib/src/tools' Making all in gen_code make[2]: Entering directory '/content/ta-lib/src/tools/gen_code' gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_common -g -O2 -MT gen_code-gen_code.o -MD -MP -MF .deps/gen_code-gen_code.Tpo -c -o gen_code-gen_code.o `test -f 'gen_code.c' || echo './'`gen_code.c gen_code.c: In function ‘printFuncHeaderDoc’: gen_code.c:3456:4: warning: format not a string literal and no format arguments [-Wformat-security] 3456 | fprintf( out, prefix ); | ^~~~~~~ mv -f .deps/gen_code-gen_code.Tpo .deps/gen_code-gen_code.Po /bin/bash ../../../libtool --tag=CC --mode=link gcc -g -O2 -L../../ta_common -L../../ta_abstract -L../../ta_func -o gen_code gen_code-gen_code.o -lta_common -lta_abstract_gc -lta_func -lm -lpthread -ldl mkdir .libs gcc -g -O2 -o gen_code gen_code-gen_code.o -L/content/ta-lib/src/ta_common -L/content/ta-lib/src/ta_abstract -L/content/ta-lib/src/ta_func /content/ta-lib/src/ta_common/.libs/libta_common.a /content/ta-lib/src/ta_abstract/.libs/libta_abstract_gc.a /content/ta-lib/src/ta_func/.libs/libta_func.a -lm -lpthread -ldl make gen_code make[3]: Entering directory '/content/ta-lib/src/tools/gen_code' make[3]: 'gen_code' is up to date. make[3]: Leaving directory '/content/ta-lib/src/tools/gen_code' cp gen_code ../../../bin make[2]: Leaving directory '/content/ta-lib/src/tools/gen_code' Making all in ta_regtest make[2]: Entering directory '/content/ta-lib/src/tools/ta_regtest' gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-ta_regtest.o -MD -MP -MF .deps/ta_regtest-ta_regtest.Tpo -c -o ta_regtest-ta_regtest.o `test -f 'ta_regtest.c' || echo './'`ta_regtest.c mv -f .deps/ta_regtest-ta_regtest.Tpo .deps/ta_regtest-ta_regtest.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_data.o -MD -MP -MF .deps/ta_regtest-test_data.Tpo -c -o ta_regtest-test_data.o `test -f 'test_data.c' || echo './'`test_data.c mv -f .deps/ta_regtest-test_data.Tpo .deps/ta_regtest-test_data.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_util.o -MD -MP -MF .deps/ta_regtest-test_util.Tpo -c -o ta_regtest-test_util.o `test -f 'test_util.c' || echo './'`test_util.c mv -f .deps/ta_regtest-test_util.Tpo .deps/ta_regtest-test_util.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_abstract.o -MD -MP -MF .deps/ta_regtest-test_abstract.Tpo -c -o ta_regtest-test_abstract.o `test -f 'test_abstract.c' || echo './'`test_abstract.c mv -f .deps/ta_regtest-test_abstract.Tpo .deps/ta_regtest-test_abstract.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_adx.o -MD -MP -MF .deps/ta_regtest-test_adx.Tpo -c -o ta_regtest-test_adx.o `test -f 'ta_test_func/test_adx.c' || echo './'`ta_test_func/test_adx.c mv -f .deps/ta_regtest-test_adx.Tpo .deps/ta_regtest-test_adx.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_mom.o -MD -MP -MF .deps/ta_regtest-test_mom.Tpo -c -o ta_regtest-test_mom.o `test -f 'ta_test_func/test_mom.c' || echo './'`ta_test_func/test_mom.c mv -f .deps/ta_regtest-test_mom.Tpo .deps/ta_regtest-test_mom.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_sar.o -MD -MP -MF .deps/ta_regtest-test_sar.Tpo -c -o ta_regtest-test_sar.o `test -f 'ta_test_func/test_sar.c' || echo './'`ta_test_func/test_sar.c mv -f .deps/ta_regtest-test_sar.Tpo .deps/ta_regtest-test_sar.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_rsi.o -MD -MP -MF .deps/ta_regtest-test_rsi.Tpo -c -o ta_regtest-test_rsi.o `test -f 'ta_test_func/test_rsi.c' || echo './'`ta_test_func/test_rsi.c mv -f .deps/ta_regtest-test_rsi.Tpo .deps/ta_regtest-test_rsi.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_candlestick.o -MD -MP -MF .deps/ta_regtest-test_candlestick.Tpo -c -o ta_regtest-test_candlestick.o `test -f 'ta_test_func/test_candlestick.c' || echo './'`ta_test_func/test_candlestick.c mv -f .deps/ta_regtest-test_candlestick.Tpo .deps/ta_regtest-test_candlestick.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_per_ema.o -MD -MP -MF .deps/ta_regtest-test_per_ema.Tpo -c -o ta_regtest-test_per_ema.o `test -f 'ta_test_func/test_per_ema.c' || echo './'`ta_test_func/test_per_ema.c mv -f .deps/ta_regtest-test_per_ema.Tpo .deps/ta_regtest-test_per_ema.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_per_hlc.o -MD -MP -MF .deps/ta_regtest-test_per_hlc.Tpo -c -o ta_regtest-test_per_hlc.o `test -f 'ta_test_func/test_per_hlc.c' || echo './'`ta_test_func/test_per_hlc.c mv -f .deps/ta_regtest-test_per_hlc.Tpo .deps/ta_regtest-test_per_hlc.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_stoch.o -MD -MP -MF .deps/ta_regtest-test_stoch.Tpo -c -o ta_regtest-test_stoch.o `test -f 'ta_test_func/test_stoch.c' || echo './'`ta_test_func/test_stoch.c mv -f .deps/ta_regtest-test_stoch.Tpo .deps/ta_regtest-test_stoch.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_macd.o -MD -MP -MF .deps/ta_regtest-test_macd.Tpo -c -o ta_regtest-test_macd.o `test -f 'ta_test_func/test_macd.c' || echo './'`ta_test_func/test_macd.c mv -f .deps/ta_regtest-test_macd.Tpo .deps/ta_regtest-test_macd.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_minmax.o -MD -MP -MF .deps/ta_regtest-test_minmax.Tpo -c -o ta_regtest-test_minmax.o `test -f 'ta_test_func/test_minmax.c' || echo './'`ta_test_func/test_minmax.c mv -f .deps/ta_regtest-test_minmax.Tpo .deps/ta_regtest-test_minmax.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_per_hlcv.o -MD -MP -MF .deps/ta_regtest-test_per_hlcv.Tpo -c -o ta_regtest-test_per_hlcv.o `test -f 'ta_test_func/test_per_hlcv.c' || echo './'`ta_test_func/test_per_hlcv.c mv -f .deps/ta_regtest-test_per_hlcv.Tpo .deps/ta_regtest-test_per_hlcv.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_1in_1out.o -MD -MP -MF .deps/ta_regtest-test_1in_1out.Tpo -c -o ta_regtest-test_1in_1out.o `test -f 'ta_test_func/test_1in_1out.c' || echo './'`ta_test_func/test_1in_1out.c mv -f .deps/ta_regtest-test_1in_1out.Tpo .deps/ta_regtest-test_1in_1out.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_1in_2out.o -MD -MP -MF .deps/ta_regtest-test_1in_2out.Tpo -c -o ta_regtest-test_1in_2out.o `test -f 'ta_test_func/test_1in_2out.c' || echo './'`ta_test_func/test_1in_2out.c mv -f .deps/ta_regtest-test_1in_2out.Tpo .deps/ta_regtest-test_1in_2out.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_per_ohlc.o -MD -MP -MF .deps/ta_regtest-test_per_ohlc.Tpo -c -o ta_regtest-test_per_ohlc.o `test -f 'ta_test_func/test_per_ohlc.c' || echo './'`ta_test_func/test_per_ohlc.c mv -f .deps/ta_regtest-test_per_ohlc.Tpo .deps/ta_regtest-test_per_ohlc.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_stddev.o -MD -MP -MF .deps/ta_regtest-test_stddev.Tpo -c -o ta_regtest-test_stddev.o `test -f 'ta_test_func/test_stddev.c' || echo './'`ta_test_func/test_stddev.c mv -f .deps/ta_regtest-test_stddev.Tpo .deps/ta_regtest-test_stddev.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_bbands.o -MD -MP -MF .deps/ta_regtest-test_bbands.Tpo -c -o ta_regtest-test_bbands.o `test -f 'ta_test_func/test_bbands.c' || echo './'`ta_test_func/test_bbands.c mv -f .deps/ta_regtest-test_bbands.Tpo .deps/ta_regtest-test_bbands.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_ma.o -MD -MP -MF .deps/ta_regtest-test_ma.Tpo -c -o ta_regtest-test_ma.o `test -f 'ta_test_func/test_ma.c' || echo './'`ta_test_func/test_ma.c mv -f .deps/ta_regtest-test_ma.Tpo .deps/ta_regtest-test_ma.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_po.o -MD -MP -MF .deps/ta_regtest-test_po.Tpo -c -o ta_regtest-test_po.o `test -f 'ta_test_func/test_po.c' || echo './'`ta_test_func/test_po.c mv -f .deps/ta_regtest-test_po.Tpo .deps/ta_regtest-test_po.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_per_hl.o -MD -MP -MF .deps/ta_regtest-test_per_hl.Tpo -c -o ta_regtest-test_per_hl.o `test -f 'ta_test_func/test_per_hl.c' || echo './'`ta_test_func/test_per_hl.c mv -f .deps/ta_regtest-test_per_hl.Tpo .deps/ta_regtest-test_per_hl.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_trange.o -MD -MP -MF .deps/ta_regtest-test_trange.Tpo -c -o ta_regtest-test_trange.o `test -f 'ta_test_func/test_trange.c' || echo './'`ta_test_func/test_trange.c mv -f .deps/ta_regtest-test_trange.Tpo .deps/ta_regtest-test_trange.Po gcc -DHAVE_CONFIG_H -I. -I../../../include -I../../ta_func -I../../ta_common/trio -I../../ta_common/mt -I../../ta_common -I../../ta_abstract -g -O2 -MT ta_regtest-test_internals.o -MD -MP -MF .deps/ta_regtest-test_internals.Tpo -c -o ta_regtest-test_internals.o `test -f 'test_internals.c' || echo './'`test_internals.c mv -f .deps/ta_regtest-test_internals.Tpo .deps/ta_regtest-test_internals.Po /bin/bash ../../../libtool --tag=CC --mode=link gcc -g -O2 -L../.. -lta_lib -lm -o ta_regtest ta_regtest-ta_regtest.o ta_regtest-test_data.o ta_regtest-test_util.o ta_regtest-test_abstract.o ta_regtest-test_adx.o ta_regtest-test_mom.o ta_regtest-test_sar.o ta_regtest-test_rsi.o ta_regtest-test_candlestick.o ta_regtest-test_per_ema.o ta_regtest-test_per_hlc.o ta_regtest-test_stoch.o ta_regtest-test_macd.o ta_regtest-test_minmax.o ta_regtest-test_per_hlcv.o ta_regtest-test_1in_1out.o ta_regtest-test_1in_2out.o ta_regtest-test_per_ohlc.o ta_regtest-test_stddev.o ta_regtest-test_bbands.o ta_regtest-test_ma.o ta_regtest-test_po.o ta_regtest-test_per_hl.o ta_regtest-test_trange.o ta_regtest-test_internals.o -lpthread -ldl mkdir .libs gcc -g -O2 -o .libs/ta_regtest ta_regtest-ta_regtest.o ta_regtest-test_data.o ta_regtest-test_util.o ta_regtest-test_abstract.o ta_regtest-test_adx.o ta_regtest-test_mom.o ta_regtest-test_sar.o ta_regtest-test_rsi.o ta_regtest-test_candlestick.o ta_regtest-test_per_ema.o ta_regtest-test_per_hlc.o ta_regtest-test_stoch.o ta_regtest-test_macd.o ta_regtest-test_minmax.o ta_regtest-test_per_hlcv.o ta_regtest-test_1in_1out.o ta_regtest-test_1in_2out.o ta_regtest-test_per_ohlc.o ta_regtest-test_stddev.o ta_regtest-test_bbands.o ta_regtest-test_ma.o ta_regtest-test_po.o ta_regtest-test_per_hl.o ta_regtest-test_trange.o ta_regtest-test_internals.o -L/content/ta-lib/src /content/ta-lib/src/.libs/libta_lib.so -lm -lpthread -ldl creating ta_regtest make[2]: Leaving directory '/content/ta-lib/src/tools/ta_regtest' make[2]: Entering directory '/content/ta-lib/src/tools' make[2]: Nothing to be done for 'all-am'. make[2]: Leaving directory '/content/ta-lib/src/tools' make[1]: Leaving directory '/content/ta-lib/src/tools' make[1]: Entering directory '/content/ta-lib' make[1]: Nothing to be done for 'all-am'. make[1]: Leaving directory '/content/ta-lib' Making install in src make[1]: Entering directory '/content/ta-lib/src' Making install in ta_abstract make[2]: Entering directory '/content/ta-lib/src/ta_abstract' make[3]: Entering directory '/content/ta-lib/src/ta_abstract' make[3]: Nothing to be done for 'install-exec-am'. test -z "/usr/include/ta-lib/" || /usr/bin/mkdir -p "/usr/include/ta-lib/" /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h' /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h' /usr/bin/install -c -m 644 '../../include/ta_abstract.h' '/usr/include/ta-lib//ta_abstract.h' make[3]: Leaving directory '/content/ta-lib/src/ta_abstract' make[2]: Leaving directory '/content/ta-lib/src/ta_abstract' Making install in ta_common make[2]: Entering directory '/content/ta-lib/src/ta_common' make[3]: Entering directory '/content/ta-lib/src/ta_common' make[3]: Nothing to be done for 'install-exec-am'. test -z "/usr/include/ta-lib/" || /usr/bin/mkdir -p "/usr/include/ta-lib/" /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h' /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h' /usr/bin/install -c -m 644 '../../include/ta_common.h' '/usr/include/ta-lib//ta_common.h' make[3]: Leaving directory '/content/ta-lib/src/ta_common' make[2]: Leaving directory '/content/ta-lib/src/ta_common' Making install in ta_func make[2]: Entering directory '/content/ta-lib/src/ta_func' make[3]: Entering directory '/content/ta-lib/src/ta_func' make[3]: Nothing to be done for 'install-exec-am'. test -z "/usr/include/ta-lib/" || /usr/bin/mkdir -p "/usr/include/ta-lib/" /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h' /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h' /usr/bin/install -c -m 644 '../../include/ta_func.h' '/usr/include/ta-lib//ta_func.h' make[3]: Leaving directory '/content/ta-lib/src/ta_func' make[2]: Leaving directory '/content/ta-lib/src/ta_func' make[2]: Entering directory '/content/ta-lib/src' make[3]: Entering directory '/content/ta-lib/src' test -z "/usr/lib" || /usr/bin/mkdir -p "/usr/lib" /bin/bash ../libtool --mode=install /usr/bin/install -c 'libta_lib.la' '/usr/lib/libta_lib.la' /usr/bin/install -c .libs/libta_lib.so.0.0.0 /usr/lib/libta_lib.so.0.0.0 (cd /usr/lib && { ln -s -f libta_lib.so.0.0.0 libta_lib.so.0 || { rm -f libta_lib.so.0 && ln -s libta_lib.so.0.0.0 libta_lib.so.0; }; }) (cd /usr/lib && { ln -s -f libta_lib.so.0.0.0 libta_lib.so || { rm -f libta_lib.so && ln -s libta_lib.so.0.0.0 libta_lib.so; }; }) /usr/bin/install -c .libs/libta_lib.lai /usr/lib/libta_lib.la /usr/bin/install -c .libs/libta_lib.a /usr/lib/libta_lib.a chmod 644 /usr/lib/libta_lib.a ranlib /usr/lib/libta_lib.a PATH="$PATH:/sbin" ldconfig -n /usr/lib ---------------------------------------------------------------------- Libraries have been installed in: /usr/lib If you ever happen to want to link against installed libraries in a given directory, LIBDIR, you must either use libtool, and specify the full pathname of the library, or use the `-LLIBDIR' flag during linking and do at least one of the following: - add LIBDIR to the `LD_LIBRARY_PATH' environment variable during execution - add LIBDIR to the `LD_RUN_PATH' environment variable during linking - use the `-Wl,--rpath -Wl,LIBDIR' linker flag - have your system administrator add LIBDIR to `/etc/ld.so.conf' See any operating system documentation about shared libraries for more information, such as the ld(1) and ld.so(8) manual pages. ---------------------------------------------------------------------- make[3]: Nothing to be done for 'install-data-am'. make[3]: Leaving directory '/content/ta-lib/src' make[2]: Leaving directory '/content/ta-lib/src' make[1]: Leaving directory '/content/ta-lib/src' Making install in src/tools make[1]: Entering directory '/content/ta-lib/src/tools' Making install in gen_code make[2]: Entering directory '/content/ta-lib/src/tools/gen_code' make gen_code make[3]: Entering directory '/content/ta-lib/src/tools/gen_code' make[3]: 'gen_code' is up to date. make[3]: Leaving directory '/content/ta-lib/src/tools/gen_code' cp gen_code ../../../bin make[3]: Entering directory '/content/ta-lib/src/tools/gen_code' make[3]: Nothing to be done for 'install-exec-am'. make[3]: Nothing to be done for 'install-data-am'. make[3]: Leaving directory '/content/ta-lib/src/tools/gen_code' make[2]: Leaving directory '/content/ta-lib/src/tools/gen_code' Making install in ta_regtest make[2]: Entering directory '/content/ta-lib/src/tools/ta_regtest' make[3]: Entering directory '/content/ta-lib/src/tools/ta_regtest' make[3]: Nothing to be done for 'install-exec-am'. make[3]: Nothing to be done for 'install-data-am'. make[3]: Leaving directory '/content/ta-lib/src/tools/ta_regtest' make[2]: Leaving directory '/content/ta-lib/src/tools/ta_regtest' make[2]: Entering directory '/content/ta-lib/src/tools' make[3]: Entering directory '/content/ta-lib/src/tools' make[3]: Nothing to be done for 'install-exec-am'. make[3]: Nothing to be done for 'install-data-am'. make[3]: Leaving directory '/content/ta-lib/src/tools' make[2]: Leaving directory '/content/ta-lib/src/tools' make[1]: Leaving directory '/content/ta-lib/src/tools' make[1]: Entering directory '/content/ta-lib' make[2]: Entering directory '/content/ta-lib' test -z "/usr/bin" || /usr/bin/mkdir -p "/usr/bin" /usr/bin/install -c 'ta-lib-config' '/usr/bin/ta-lib-config' make[2]: Nothing to be done for 'install-data-am'. make[2]: Leaving directory '/content/ta-lib' make[1]: Leaving directory '/content/ta-lib' Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting Ta-Lib Downloading TA-Lib-0.4.26.tar.gz (272 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 272.6/272.6 kB 5.4 MB/s eta 0:00:00 Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from Ta-Lib) (1.22.4) Building wheels for collected packages: Ta-Lib Building wheel for Ta-Lib (pyproject.toml) ... done Created wheel for Ta-Lib: filename=TA_Lib-0.4.26-cp310-cp310-linux_x86_64.whl size=2238993 sha256=e87444ea275eb02e8ad06402808d833dbfce24cf28a14f5fc793682629d129a3 Stored in directory: /root/.cache/pip/wheels/ad/be/fe/93a0b4e4efd7fbb963157b79d512c747e935be08fc7b9e3a53 Successfully built Ta-Lib Installing collected packages: Ta-Lib Successfully installed Ta-Lib-0.4.26
In [71]:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr, spearmanr
from talib import RSI, BBANDS, MACD, ATR
In [72]:
MONTH = 21
YEAR = 12 * MONTH
In [73]:
START = '2013-01-01'
END = '2017-12-31'
In [74]:
sns.set_style('whitegrid')
idx = pd.IndexSlice
Loading Quandl Wiki Stock Prices & Meta Data¶
In [75]:
ohlcv = ['adj_open', 'adj_close', 'adj_low', 'adj_high', 'adj_volume']
In [89]:
DATA_STORE = '/content/assets.h5'
In [88]:
!pwd
/content/ta-lib
In [135]:
with pd.HDFStore(DATA_STORE) as store:
prices = (store['quandl/wiki/prices']
.loc[idx[START:END, :], ohlcv]
.rename(columns=lambda x: x.replace('adj_', ''))
.assign(volume=lambda x: x.volume.div(1000))
.swaplevel()
.sort_index())
stocks = (store['us_equities/stocks']
.loc[:, ['marketcap', 'ipoyear', 'sector']])
Remove stocks with few observations¶
In [136]:
# want at least 2 years of data
min_obs = 2 * YEAR
# have this much per ticker
nobs = prices.groupby(level='ticker').size()
# keep those that exceed the limit
keep = nobs[nobs > min_obs].index
prices = prices.loc[idx[keep, :], :]
Align price and meta data¶
In [137]:
stocks = stocks[~stocks.index.duplicated() & stocks.sector.notnull()]
stocks.sector = stocks.sector.str.lower().str.replace(' ', '_')
stocks.index.name = 'ticker'
In [138]:
shared = (prices.index.get_level_values('ticker').unique()
.intersection(stocks.index))
stocks = stocks.loc[shared, :]
prices = prices.loc[idx[shared, :], :]
In [139]:
prices.info(show_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 2904233 non-null float64
1 close 2904233 non-null float64
2 low 2904233 non-null float64
3 high 2904233 non-null float64
4 volume 2904233 non-null float64
dtypes: float64(5)
memory usage: 122.6+ MB
In [140]:
stocks.info(show_counts=True)
<class 'pandas.core.frame.DataFrame'> Index: 2348 entries, A to ZUMZ Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 marketcap 2345 non-null float64 1 ipoyear 1026 non-null float64 2 sector 2348 non-null object dtypes: float64(2), object(1) memory usage: 73.4+ KB
In [141]:
stocks.sector.value_counts()
Out[141]:
consumer_services 440 finance 393 technology 297 health_care 297 capital_goods 227 basic_industries 138 consumer_non-durables 126 energy 123 public_utilities 105 consumer_durables 78 miscellaneous 69 transportation 55 Name: sector, dtype: int64
Optional: persist intermediate results:
In [142]:
# with pd.HDFStore('tmp.h5') as store:
# store.put('prices', prices)
# store.put('stocks', stocks)
In [143]:
# with pd.HDFStore('tmp.h5') as store:
# prices = store['prices']
# stocks = store['stocks']
Compute Rolling Average Dollar Volume¶
In [144]:
# compute dollar volume to determine universe
prices['dollar_vol'] = prices[['close', 'volume']].prod(axis=1)
In [145]:
prices['dollar_vol_1m'] = prices.groupby('ticker')['dollar_vol'].rolling(window=21).mean().values
In [146]:
prices.info(show_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 2904233 non-null float64
1 close 2904233 non-null float64
2 low 2904233 non-null float64
3 high 2904233 non-null float64
4 volume 2904233 non-null float64
5 dollar_vol 2904233 non-null float64
6 dollar_vol_1m 2857273 non-null float64
dtypes: float64(7)
memory usage: 166.9+ MB
In [147]:
prices['dollar_vol_rank'] = (prices.groupby('date')
.dollar_vol_1m
.rank(ascending=False))
In [148]:
prices.info(show_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 2904233 non-null float64
1 close 2904233 non-null float64
2 low 2904233 non-null float64
3 high 2904233 non-null float64
4 volume 2904233 non-null float64
5 dollar_vol 2904233 non-null float64
6 dollar_vol_1m 2857273 non-null float64
7 dollar_vol_rank 2857273 non-null float64
dtypes: float64(8)
memory usage: 189.1+ MB
Add some Basic Factors¶
Compute the Relative Strength Index¶
In [149]:
prices['rsi'] = prices.groupby(level='ticker').close.apply(RSI)
In [150]:
ax = sns.distplot(prices.rsi.dropna())
ax.axvline(30, ls='--', lw=1, c='k')
ax.axvline(70, ls='--', lw=1, c='k')
ax.set_title('RSI Distribution with Signal Threshold')
plt.tight_layout();
Compute Bollinger Bands¶
In [151]:
def compute_bb(close):
high, mid, low = BBANDS(close, timeperiod=20)
return pd.DataFrame({'bb_high': high, 'bb_low': low}, index=close.index)
In [152]:
prices = (prices.join(prices
.groupby(level='ticker')
.close
.apply(compute_bb)))
In [153]:
prices['bb_high'] = prices.bb_high.sub(prices.close).div(prices.bb_high).apply(np.log1p)
prices['bb_low'] = prices.close.sub(prices.bb_low).div(prices.close).apply(np.log1p)
In [154]:
fig, axes = plt.subplots(ncols=2, figsize=(15, 5))
sns.distplot(prices.loc[prices.dollar_vol_rank<100, 'bb_low'].dropna(), ax=axes[0])
sns.distplot(prices.loc[prices.dollar_vol_rank<100, 'bb_high'].dropna(), ax=axes[1])
plt.tight_layout();
Compute Average True Range¶
In [155]:
def compute_atr(stock_data):
df = ATR(stock_data.high, stock_data.low,
stock_data.close, timeperiod=14)
return df.sub(df.mean()).div(df.std())
In [156]:
prices['atr'] = (prices.groupby('ticker', group_keys=False)
.apply(compute_atr))
In [157]:
sns.distplot(prices[prices.dollar_vol_rank<50].atr.dropna());
Compute Moving Average Convergence/Divergence¶
In [158]:
def compute_macd(close):
macd = MACD(close)[0]
return (macd - np.mean(macd))/np.std(macd)
In [159]:
prices['macd'] = (prices
.groupby('ticker', group_keys=False)
.close
.apply(compute_macd))
In [160]:
prices.macd.describe(percentiles=[.001, .01, .02, .03, .04, .05, .95, .96, .97, .98, .99, .999]).apply(lambda x: f'{x:,.1f}')
Out[160]:
count 2,826,749.0 mean 0.0 std 1.0 min -10.5 0.1% -4.1 1% -2.6 2% -2.2 3% -2.0 4% -1.8 5% -1.6 50% 0.0 95% 1.6 96% 1.7 97% 1.9 98% 2.1 99% 2.6 99.9% 4.0 max 8.7 Name: macd, dtype: object
In [161]:
sns.distplot(prices[prices.dollar_vol_rank<100].macd.dropna());
Compute Lagged Returns¶
In [162]:
lags = [1, 5, 10, 21, 42, 63]
In [163]:
returns = prices.groupby(level='ticker').close.pct_change()
percentiles=[.0001, .001, .01]
percentiles+= [1-p for p in percentiles]
returns.describe(percentiles=percentiles).iloc[2:].to_frame('percentiles').style.format(lambda x: f'{x:,.2%}')
Out[163]:
| percentiles | |
|---|---|
| std | 9.37% |
| min | -87.62% |
| 0.01% | -33.40% |
| 0.1% | -14.78% |
| 1% | -6.52% |
| 50% | 0.04% |
| 99% | 6.98% |
| 99.9% | 17.03% |
| 99.99% | 39.25% |
| max | 10,280.40% |
In [164]:
q = 0.0001
Winsorize outliers¶
In [165]:
for lag in lags:
prices[f'return_{lag}d'] = (prices.groupby(level='ticker').close
.pct_change(lag)
.pipe(lambda x: x.clip(lower=x.quantile(q),
upper=x.quantile(1 - q)))
.add(1)
.pow(1 / lag)
.sub(1)
)
Shift lagged returns¶
In [166]:
for t in [1, 2, 3, 4, 5]:
for lag in [1, 5, 10, 21]:
prices[f'return_{lag}d_lag{t}'] = (prices.groupby(level='ticker')
[f'return_{lag}d'].shift(t * lag))
Compute Forward Returns¶
In [167]:
for t in [1, 5, 10, 21]:
prices[f'target_{t}d'] = prices.groupby(level='ticker')[f'return_{t}d'].shift(-t)
Combine Price and Meta Data¶
In [168]:
prices = prices.join(stocks[['sector']])
Create time and sector dummy variables¶
In [169]:
prices['year'] = prices.index.get_level_values('date').year
prices['month'] = prices.index.get_level_values('date').month
In [170]:
prices.info(null_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))
Data columns (total 46 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 2904233 non-null float64
1 close 2904233 non-null float64
2 low 2904233 non-null float64
3 high 2904233 non-null float64
4 volume 2904233 non-null float64
5 dollar_vol 2904233 non-null float64
6 dollar_vol_1m 2857273 non-null float64
7 dollar_vol_rank 2857273 non-null float64
8 rsi 2871361 non-null float64
9 bb_high 2859618 non-null float64
10 bb_low 2859585 non-null float64
11 atr 2871361 non-null float64
12 macd 2826749 non-null float64
13 return_1d 2901885 non-null float64
14 return_5d 2892493 non-null float64
15 return_10d 2880753 non-null float64
16 return_21d 2854925 non-null float64
17 return_42d 2805617 non-null float64
18 return_63d 2756309 non-null float64
19 return_1d_lag1 2899537 non-null float64
20 return_5d_lag1 2880753 non-null float64
21 return_10d_lag1 2857273 non-null float64
22 return_21d_lag1 2805617 non-null float64
23 return_1d_lag2 2897189 non-null float64
24 return_5d_lag2 2869013 non-null float64
25 return_10d_lag2 2833793 non-null float64
26 return_21d_lag2 2756309 non-null float64
27 return_1d_lag3 2894841 non-null float64
28 return_5d_lag3 2857273 non-null float64
29 return_10d_lag3 2810313 non-null float64
30 return_21d_lag3 2707001 non-null float64
31 return_1d_lag4 2892493 non-null float64
32 return_5d_lag4 2845533 non-null float64
33 return_10d_lag4 2786833 non-null float64
34 return_21d_lag4 2657693 non-null float64
35 return_1d_lag5 2890145 non-null float64
36 return_5d_lag5 2833793 non-null float64
37 return_10d_lag5 2763353 non-null float64
38 return_21d_lag5 2608385 non-null float64
39 target_1d 2901885 non-null float64
40 target_5d 2892493 non-null float64
41 target_10d 2880753 non-null float64
42 target_21d 2854925 non-null float64
43 sector 2904233 non-null object
44 year 2904233 non-null int64
45 month 2904233 non-null int64
dtypes: float64(43), int64(2), object(1)
memory usage: 1.1+ GB
In [171]:
prices.assign(sector=pd.factorize(prices.sector, sort=True)[0]).to_hdf('/content/data.h5', 'model_data/no_dummies')
In [172]:
prices = pd.get_dummies(prices,
columns=['year', 'month', 'sector'],
prefix=['year', 'month', ''],
prefix_sep=['_', '_', ''],
drop_first=True)
In [173]:
prices.info(null_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))
Data columns (total 69 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 open 2904233 non-null float64
1 close 2904233 non-null float64
2 low 2904233 non-null float64
3 high 2904233 non-null float64
4 volume 2904233 non-null float64
5 dollar_vol 2904233 non-null float64
6 dollar_vol_1m 2857273 non-null float64
7 dollar_vol_rank 2857273 non-null float64
8 rsi 2871361 non-null float64
9 bb_high 2859618 non-null float64
10 bb_low 2859585 non-null float64
11 atr 2871361 non-null float64
12 macd 2826749 non-null float64
13 return_1d 2901885 non-null float64
14 return_5d 2892493 non-null float64
15 return_10d 2880753 non-null float64
16 return_21d 2854925 non-null float64
17 return_42d 2805617 non-null float64
18 return_63d 2756309 non-null float64
19 return_1d_lag1 2899537 non-null float64
20 return_5d_lag1 2880753 non-null float64
21 return_10d_lag1 2857273 non-null float64
22 return_21d_lag1 2805617 non-null float64
23 return_1d_lag2 2897189 non-null float64
24 return_5d_lag2 2869013 non-null float64
25 return_10d_lag2 2833793 non-null float64
26 return_21d_lag2 2756309 non-null float64
27 return_1d_lag3 2894841 non-null float64
28 return_5d_lag3 2857273 non-null float64
29 return_10d_lag3 2810313 non-null float64
30 return_21d_lag3 2707001 non-null float64
31 return_1d_lag4 2892493 non-null float64
32 return_5d_lag4 2845533 non-null float64
33 return_10d_lag4 2786833 non-null float64
34 return_21d_lag4 2657693 non-null float64
35 return_1d_lag5 2890145 non-null float64
36 return_5d_lag5 2833793 non-null float64
37 return_10d_lag5 2763353 non-null float64
38 return_21d_lag5 2608385 non-null float64
39 target_1d 2901885 non-null float64
40 target_5d 2892493 non-null float64
41 target_10d 2880753 non-null float64
42 target_21d 2854925 non-null float64
43 year_2014 2904233 non-null uint8
44 year_2015 2904233 non-null uint8
45 year_2016 2904233 non-null uint8
46 year_2017 2904233 non-null uint8
47 month_2 2904233 non-null uint8
48 month_3 2904233 non-null uint8
49 month_4 2904233 non-null uint8
50 month_5 2904233 non-null uint8
51 month_6 2904233 non-null uint8
52 month_7 2904233 non-null uint8
53 month_8 2904233 non-null uint8
54 month_9 2904233 non-null uint8
55 month_10 2904233 non-null uint8
56 month_11 2904233 non-null uint8
57 month_12 2904233 non-null uint8
58 capital_goods 2904233 non-null uint8
59 consumer_durables 2904233 non-null uint8
60 consumer_non-durables 2904233 non-null uint8
61 consumer_services 2904233 non-null uint8
62 energy 2904233 non-null uint8
63 finance 2904233 non-null uint8
64 health_care 2904233 non-null uint8
65 miscellaneous 2904233 non-null uint8
66 public_utilities 2904233 non-null uint8
67 technology 2904233 non-null uint8
68 transportation 2904233 non-null uint8
dtypes: float64(43), uint8(26)
memory usage: 1.1+ GB
Store Model Data¶
In [174]:
prices.to_hdf('/content/data.h5', 'model_data')
Explore Data¶
Plot Factors¶
In [175]:
target = 'target_5d'
top100 = prices[prices.dollar_vol_rank<100].copy()
RSI¶
In [176]:
top100.loc[:, 'rsi_signal'] = pd.cut(top100.rsi, bins=[0, 30, 70, 100])
In [177]:
top100.groupby('rsi_signal')['target_5d'].describe()
Out[177]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| rsi_signal | ||||||||
| (0, 30] | 4,209.00 | 0.00 | 0.01 | -0.07 | -0.00 | 0.00 | 0.01 | 0.06 |
| (30, 70] | 107,244.00 | 0.00 | 0.01 | -0.17 | -0.00 | 0.00 | 0.00 | 0.08 |
| (70, 100] | 10,634.00 | 0.00 | 0.01 | -0.09 | -0.00 | 0.00 | 0.00 | 0.06 |
Bollinger Bands¶
In [178]:
metric = 'bb_low'
j=sns.jointplot(x=metric, y=target, data=top100)
df = top100[[metric, target]].dropna()
r, p = spearmanr(df[metric], df[target])
print(f'{r:,.2%} ({p:.2%})')
-2.68% (0.00%)
In [179]:
metric = 'bb_high'
j=sns.jointplot(x=metric, y=target, data=top100)
df = top100[[metric, target]].dropna()
r, p = spearmanr(df[metric], df[target])
print(f'{r:,.2%} ({p:.2%})')
4.21% (0.00%)
ATR¶
In [180]:
metric = 'atr'
j=sns.jointplot(x=metric, y=target, data=top100)
df = top100[[metric, target]].dropna()
r, p = spearmanr(df[metric], df[target])
print(f'{r:,.2%} ({p:.2%})')
0.07% (80.08%)
MACD¶
In [181]:
metric = 'macd'
j=sns.jointplot(x=metric, y=target, data=top100)
df = top100[[metric, target]].dropna()
r, p = spearmanr(df[metric], df[target])
print(f'{r:,.2%} ({p:.2%})')
-4.72% (0.00%)
statsmodels를 사용한 선형 OLS 회귀 분석¶
- 04_statistical_inference_of_stock_returns_with_statsmodels.ipynb
Statistical inference of stock returns with linear regression¶
Imports & Settings¶
In [182]:
import warnings
warnings.filterwarnings('ignore')
In [183]:
%matplotlib inline
import pandas as pd
from statsmodels.api import OLS, add_constant, graphics
from statsmodels.graphics.tsaplots import plot_acf
from scipy.stats import norm
import seaborn as sns
import matplotlib.pyplot as plt
In [184]:
sns.set_style('whitegrid')
idx = pd.IndexSlice
Load Data¶
In [185]:
with pd.HDFStore('/content/data.h5') as store:
data = (store['model_data']
.dropna()
.drop(['open', 'close', 'low', 'high'], axis=1))
Select Investment Universe¶
In [186]:
data = data[data.dollar_vol_rank<100]
In [187]:
data.info(null_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 109675 entries, ('AAL', Timestamp('2013-07-25 00:00:00')) to ('ZTS', Timestamp('2014-12-04 00:00:00'))
Data columns (total 65 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 volume 109675 non-null float64
1 dollar_vol 109675 non-null float64
2 dollar_vol_1m 109675 non-null float64
3 dollar_vol_rank 109675 non-null float64
4 rsi 109675 non-null float64
5 bb_high 109675 non-null float64
6 bb_low 109675 non-null float64
7 atr 109675 non-null float64
8 macd 109675 non-null float64
9 return_1d 109675 non-null float64
10 return_5d 109675 non-null float64
11 return_10d 109675 non-null float64
12 return_21d 109675 non-null float64
13 return_42d 109675 non-null float64
14 return_63d 109675 non-null float64
15 return_1d_lag1 109675 non-null float64
16 return_5d_lag1 109675 non-null float64
17 return_10d_lag1 109675 non-null float64
18 return_21d_lag1 109675 non-null float64
19 return_1d_lag2 109675 non-null float64
20 return_5d_lag2 109675 non-null float64
21 return_10d_lag2 109675 non-null float64
22 return_21d_lag2 109675 non-null float64
23 return_1d_lag3 109675 non-null float64
24 return_5d_lag3 109675 non-null float64
25 return_10d_lag3 109675 non-null float64
26 return_21d_lag3 109675 non-null float64
27 return_1d_lag4 109675 non-null float64
28 return_5d_lag4 109675 non-null float64
29 return_10d_lag4 109675 non-null float64
30 return_21d_lag4 109675 non-null float64
31 return_1d_lag5 109675 non-null float64
32 return_5d_lag5 109675 non-null float64
33 return_10d_lag5 109675 non-null float64
34 return_21d_lag5 109675 non-null float64
35 target_1d 109675 non-null float64
36 target_5d 109675 non-null float64
37 target_10d 109675 non-null float64
38 target_21d 109675 non-null float64
39 year_2014 109675 non-null uint8
40 year_2015 109675 non-null uint8
41 year_2016 109675 non-null uint8
42 year_2017 109675 non-null uint8
43 month_2 109675 non-null uint8
44 month_3 109675 non-null uint8
45 month_4 109675 non-null uint8
46 month_5 109675 non-null uint8
47 month_6 109675 non-null uint8
48 month_7 109675 non-null uint8
49 month_8 109675 non-null uint8
50 month_9 109675 non-null uint8
51 month_10 109675 non-null uint8
52 month_11 109675 non-null uint8
53 month_12 109675 non-null uint8
54 capital_goods 109675 non-null uint8
55 consumer_durables 109675 non-null uint8
56 consumer_non-durables 109675 non-null uint8
57 consumer_services 109675 non-null uint8
58 energy 109675 non-null uint8
59 finance 109675 non-null uint8
60 health_care 109675 non-null uint8
61 miscellaneous 109675 non-null uint8
62 public_utilities 109675 non-null uint8
63 technology 109675 non-null uint8
64 transportation 109675 non-null uint8
dtypes: float64(39), uint8(26)
memory usage: 36.5+ MB
Create Model Data¶
In [188]:
y = data.filter(like='target')
X = data.drop(y.columns, axis=1)
X = X.drop(['dollar_vol', 'dollar_vol_rank', 'volume', 'consumer_durables'], axis=1)
Explore Data¶
In [189]:
sns.clustermap(y.corr(), cmap=sns.diverging_palette(h_neg=20, h_pos=220), center=0, annot=True, fmt='.2%');
In [190]:
sns.clustermap(X.corr(), cmap=sns.diverging_palette(h_neg=20, h_pos=220), center=0);
plt.gcf().set_size_inches((14, 14))
In [191]:
corr_mat = X.corr().stack().reset_index()
corr_mat.columns=['var1', 'var2', 'corr']
corr_mat = corr_mat[corr_mat.var1!=corr_mat.var2].sort_values(by='corr', ascending=False)
In [192]:
corr_mat.head().append(corr_mat.tail())
Out[192]:
| var1 | var2 | corr | |
|---|---|---|---|
| 581 | return_42d | return_63d | 0.84 |
| 637 | return_63d | return_42d | 0.84 |
| 286 | macd | rsi | 0.82 |
| 62 | rsi | macd | 0.82 |
| 518 | return_21d | macd | 0.79 |
| 515 | return_21d | bb_high | -0.63 |
| 122 | bb_high | return_10d | -0.69 |
| 458 | return_10d | bb_high | -0.69 |
| 59 | rsi | bb_high | -0.70 |
| 115 | bb_high | rsi | -0.70 |
In [193]:
y.boxplot();
Linear Regression for Statistical Inference: OLS with statsmodels¶
Ticker-wise standardization¶
statsmodels warns of high design matrix condition numbers. This can arise when the variables are not standardized and the Eigenvalues differ due to scaling. The following step avoids this warning.
In [194]:
sectors = X.iloc[:, -10:]
X = (X.drop(sectors.columns, axis=1)
.groupby(level='ticker')
.transform(lambda x: (x - x.mean()) / x.std())
.join(sectors)
.fillna(0))
1-Day Returns¶
In [195]:
target = 'target_1d'
model = OLS(endog=y[target], exog=add_constant(X))
trained_model = model.fit()
print(trained_model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: target_1d R-squared: 0.010
Model: OLS Adj. R-squared: 0.009
Method: Least Squares F-statistic: 19.03
Date: Sat, 24 Jun 2023 Prob (F-statistic): 9.43e-189
Time: 16:50:07 Log-Likelihood: 2.8852e+05
No. Observations: 109675 AIC: -5.769e+05
Df Residuals: 109617 BIC: -5.764e+05
Df Model: 57
Covariance Type: nonrobust
=========================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------
const -0.0002 0.000 -0.793 0.428 -0.001 0.000
dollar_vol_1m -0.0004 6.88e-05 -5.214 0.000 -0.000 -0.000
rsi 0.0002 0.000 0.978 0.328 -0.000 0.001
bb_high 0.0002 0.000 0.927 0.354 -0.000 0.001
bb_low 0.0006 0.000 2.940 0.003 0.000 0.001
atr -3.752e-05 7.44e-05 -0.504 0.614 -0.000 0.000
macd -0.0004 0.000 -1.835 0.066 -0.001 2.96e-05
return_1d 0.0029 0.000 9.684 0.000 0.002 0.003
return_5d -0.0019 0.001 -2.015 0.044 -0.004 -5.08e-05
return_10d -0.0064 0.001 -6.443 0.000 -0.008 -0.004
return_21d 0.0028 0.000 6.242 0.000 0.002 0.004
return_42d -0.0036 0.001 -5.995 0.000 -0.005 -0.002
return_63d -0.0019 0.000 -4.241 0.000 -0.003 -0.001
return_1d_lag1 0.0027 0.000 8.998 0.000 0.002 0.003
return_5d_lag1 0.0048 0.001 7.033 0.000 0.003 0.006
return_10d_lag1 -0.0010 0.001 -0.896 0.370 -0.003 0.001
return_21d_lag1 0.0029 0.000 7.667 0.000 0.002 0.004
return_1d_lag2 0.0027 0.000 9.177 0.000 0.002 0.003
return_5d_lag2 0.0009 0.001 1.141 0.254 -0.001 0.002
return_10d_lag2 8.065e-05 0.001 0.097 0.923 -0.002 0.002
return_21d_lag2 0.0003 0.000 0.857 0.392 -0.000 0.001
return_1d_lag3 0.0027 0.000 8.973 0.000 0.002 0.003
return_5d_lag3 0.0015 0.001 1.919 0.055 -3.15e-05 0.003
return_10d_lag3 0.0005 0.000 3.457 0.001 0.000 0.001
return_21d_lag3 -0.0002 5.55e-05 -3.739 0.000 -0.000 -9.88e-05
return_1d_lag4 0.0030 0.000 9.976 0.000 0.002 0.004
return_5d_lag4 0.0006 0.001 1.057 0.290 -0.001 0.002
return_10d_lag4 0.0004 0.000 3.845 0.000 0.000 0.001
return_21d_lag4 4.323e-05 5.5e-05 0.786 0.432 -6.46e-05 0.000
return_1d_lag5 -8.924e-05 6.19e-05 -1.441 0.149 -0.000 3.21e-05
return_5d_lag5 0.0001 0.001 0.241 0.810 -0.001 0.001
return_10d_lag5 0.0007 9.95e-05 7.235 0.000 0.001 0.001
return_21d_lag5 9.403e-05 5.48e-05 1.716 0.086 -1.34e-05 0.000
year_2014 -0.0004 8.38e-05 -4.430 0.000 -0.001 -0.000
year_2015 -0.0006 9.15e-05 -6.804 0.000 -0.001 -0.000
year_2016 -0.0005 8.95e-05 -5.113 0.000 -0.001 -0.000
year_2017 -0.0002 8.83e-05 -2.024 0.043 -0.000 -5.66e-06
month_2 0.0010 7.21e-05 14.079 0.000 0.001 0.001
month_3 0.0003 7.44e-05 4.637 0.000 0.000 0.000
month_4 0.0005 7.36e-05 7.053 0.000 0.000 0.001
month_5 0.0005 7.25e-05 6.741 0.000 0.000 0.001
month_6 0.0004 7.34e-05 5.696 0.000 0.000 0.001
month_7 0.0006 7.63e-05 8.415 0.000 0.000 0.001
month_8 6.821e-05 7.7e-05 0.886 0.375 -8.26e-05 0.000
month_9 0.0004 7.59e-05 5.017 0.000 0.000 0.001
month_10 0.0007 7.8e-05 8.524 0.000 0.001 0.001
month_11 0.0006 7.6e-05 7.697 0.000 0.000 0.001
month_12 0.0004 7.42e-05 5.153 0.000 0.000 0.001
capital_goods 0.0010 0.000 2.714 0.007 0.000 0.002
consumer_non-durables 0.0007 0.000 1.793 0.073 -6.33e-05 0.001
consumer_services 0.0008 0.000 2.234 0.025 9.29e-05 0.001
energy 0.0003 0.000 0.913 0.361 -0.000 0.001
finance 0.0009 0.000 2.533 0.011 0.000 0.002
health_care 0.0007 0.000 2.065 0.039 3.51e-05 0.001
miscellaneous 0.0011 0.000 2.565 0.010 0.000 0.002
public_utilities 0.0003 0.000 0.749 0.454 -0.001 0.001
technology 0.0011 0.000 3.285 0.001 0.000 0.002
transportation 0.0010 0.000 2.426 0.015 0.000 0.002
==============================================================================
Omnibus: 29139.970 Durbin-Watson: 2.010
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2800989.218
Skew: -0.105 Prob(JB): 0.00
Kurtosis: 27.757 Cond. No. 80.7
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
5-Day Returns¶
In [196]:
target = 'target_5d'
model = OLS(endog=y[target], exog=add_constant(X))
trained_model = model.fit()
print(trained_model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: target_5d R-squared: 0.031
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 61.86
Date: Sat, 24 Jun 2023 Prob (F-statistic): 0.00
Time: 16:50:08 Log-Likelihood: 3.7883e+05
No. Observations: 109675 AIC: -7.575e+05
Df Residuals: 109617 BIC: -7.570e+05
Df Model: 57
Covariance Type: nonrobust
=========================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------
const -0.0005 0.000 -3.412 0.001 -0.001 -0.000
dollar_vol_1m -0.0003 3.02e-05 -8.790 0.000 -0.000 -0.000
rsi 0.0005 8.43e-05 5.477 0.000 0.000 0.001
bb_high 0.0010 9.88e-05 10.353 0.000 0.001 0.001
bb_low -0.0004 9.46e-05 -4.550 0.000 -0.001 -0.000
atr -9.8e-05 3.27e-05 -2.999 0.003 -0.000 -3.4e-05
macd -0.0004 0.000 -3.880 0.000 -0.001 -0.000
return_1d 0.0010 0.000 7.468 0.000 0.001 0.001
return_5d 0.0012 0.000 2.947 0.003 0.000 0.002
return_10d -0.0038 0.000 -8.639 0.000 -0.005 -0.003
return_21d 0.0021 0.000 10.714 0.000 0.002 0.003
return_42d -0.0024 0.000 -9.124 0.000 -0.003 -0.002
return_63d -0.0017 0.000 -8.800 0.000 -0.002 -0.001
return_1d_lag1 0.0009 0.000 7.281 0.000 0.001 0.001
return_5d_lag1 0.0035 0.000 11.632 0.000 0.003 0.004
return_10d_lag1 0.0007 0.000 1.546 0.122 -0.000 0.002
return_21d_lag1 0.0020 0.000 11.876 0.000 0.002 0.002
return_1d_lag2 0.0010 0.000 7.493 0.000 0.001 0.001
return_5d_lag2 -6.79e-06 0.000 -0.020 0.984 -0.001 0.001
return_10d_lag2 -0.0003 0.000 -0.918 0.359 -0.001 0.000
return_21d_lag2 0.0006 0.000 4.309 0.000 0.000 0.001
return_1d_lag3 0.0010 0.000 7.844 0.000 0.001 0.001
return_5d_lag3 0.0001 0.000 0.397 0.691 -0.001 0.001
return_10d_lag3 0.0005 6.4e-05 8.171 0.000 0.000 0.001
return_21d_lag3 -0.0002 2.44e-05 -7.933 0.000 -0.000 -0.000
return_1d_lag4 0.0010 0.000 7.887 0.000 0.001 0.001
return_5d_lag4 0.0007 0.000 2.443 0.015 0.000 0.001
return_10d_lag4 4.558e-05 4.51e-05 1.012 0.312 -4.27e-05 0.000
return_21d_lag4 8.989e-05 2.41e-05 3.724 0.000 4.26e-05 0.000
return_1d_lag5 1.138e-05 2.72e-05 0.419 0.675 -4.19e-05 6.46e-05
return_5d_lag5 0.0005 0.000 1.848 0.065 -2.91e-05 0.001
return_10d_lag5 0.0004 4.37e-05 8.446 0.000 0.000 0.000
return_21d_lag5 0.0002 2.4e-05 7.811 0.000 0.000 0.000
year_2014 -0.0003 3.68e-05 -9.407 0.000 -0.000 -0.000
year_2015 -0.0005 4.02e-05 -13.632 0.000 -0.001 -0.000
year_2016 -0.0003 3.93e-05 -8.157 0.000 -0.000 -0.000
year_2017 -0.0002 3.88e-05 -4.454 0.000 -0.000 -9.67e-05
month_2 0.0009 3.16e-05 27.541 0.000 0.001 0.001
month_3 0.0001 3.27e-05 3.949 0.000 6.5e-05 0.000
month_4 0.0003 3.23e-05 9.721 0.000 0.000 0.000
month_5 0.0005 3.18e-05 14.481 0.000 0.000 0.001
month_6 0.0002 3.22e-05 7.201 0.000 0.000 0.000
month_7 0.0004 3.35e-05 13.321 0.000 0.000 0.001
month_8 3.574e-05 3.38e-05 1.058 0.290 -3.05e-05 0.000
month_9 0.0003 3.33e-05 7.745 0.000 0.000 0.000
month_10 0.0004 3.42e-05 12.425 0.000 0.000 0.000
month_11 0.0005 3.33e-05 13.788 0.000 0.000 0.001
month_12 0.0001 3.26e-05 4.388 0.000 7.91e-05 0.000
capital_goods 0.0011 0.000 6.699 0.000 0.001 0.001
consumer_non-durables 0.0009 0.000 5.179 0.000 0.001 0.001
consumer_services 0.0009 0.000 6.038 0.000 0.001 0.001
energy 0.0004 0.000 2.488 0.013 8.11e-05 0.001
finance 0.0010 0.000 6.510 0.000 0.001 0.001
health_care 0.0008 0.000 5.240 0.000 0.000 0.001
miscellaneous 0.0012 0.000 6.354 0.000 0.001 0.002
public_utilities 0.0003 0.000 1.864 0.062 -1.77e-05 0.001
technology 0.0011 0.000 7.760 0.000 0.001 0.001
transportation 0.0010 0.000 5.920 0.000 0.001 0.001
==============================================================================
Omnibus: 43640.257 Durbin-Watson: 0.436
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2362450.641
Skew: -1.138 Prob(JB): 0.00
Kurtosis: 25.623 Cond. No. 80.7
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Obtain the residuals¶
In [197]:
preds = trained_model.predict(add_constant(X))
residuals = y[target] - preds
In [198]:
fig, axes = plt.subplots(ncols=2, figsize=(14,4))
sns.distplot(residuals, fit=norm, ax=axes[0], axlabel='Residuals', label='Residuals')
axes[0].set_title('Residual Distribution')
axes[0].legend()
plot_acf(residuals, lags=10, zero=False, ax=axes[1], title='Residual Autocorrelation')
axes[1].set_xlabel('Lags')
sns.despine()
fig.tight_layout();
10-Day Returns¶
In [199]:
target = 'target_10d'
model = OLS(endog=y[target], exog=add_constant(X))
trained_model = model.fit()
print(trained_model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: target_10d R-squared: 0.043
Model: OLS Adj. R-squared: 0.042
Method: Least Squares F-statistic: 85.38
Date: Sat, 24 Jun 2023 Prob (F-statistic): 0.00
Time: 16:50:16 Log-Likelihood: 4.1892e+05
No. Observations: 109675 AIC: -8.377e+05
Df Residuals: 109617 BIC: -8.372e+05
Df Model: 57
Covariance Type: nonrobust
=========================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------
const -0.0004 9.5e-05 -3.922 0.000 -0.001 -0.000
dollar_vol_1m -0.0001 2.1e-05 -6.800 0.000 -0.000 -0.000
rsi 0.0002 5.85e-05 2.759 0.006 4.67e-05 0.000
bb_high 0.0006 6.85e-05 9.273 0.000 0.001 0.001
bb_low -0.0002 6.56e-05 -2.847 0.004 -0.000 -5.82e-05
atr -0.0002 2.27e-05 -7.328 0.000 -0.000 -0.000
macd -0.0001 7.22e-05 -1.908 0.056 -0.000 3.76e-06
return_1d 0.0005 9.01e-05 6.046 0.000 0.000 0.001
return_5d 5.98e-05 0.000 0.212 0.832 -0.000 0.001
return_10d -0.0008 0.000 -2.741 0.006 -0.001 -0.000
return_21d 0.0017 0.000 11.902 0.000 0.001 0.002
return_42d -0.0025 0.000 -13.475 0.000 -0.003 -0.002
return_63d -0.0014 0.000 -10.322 0.000 -0.002 -0.001
return_1d_lag1 0.0005 8.98e-05 5.980 0.000 0.000 0.001
return_5d_lag1 0.0013 0.000 6.090 0.000 0.001 0.002
return_10d_lag1 0.0009 0.000 2.615 0.009 0.000 0.002
return_21d_lag1 0.0017 0.000 14.947 0.000 0.002 0.002
return_1d_lag2 0.0005 8.99e-05 5.956 0.000 0.000 0.001
return_5d_lag2 6.813e-05 0.000 0.294 0.769 -0.000 0.001
return_10d_lag2 0.0002 0.000 0.690 0.490 -0.000 0.001
return_21d_lag2 0.0002 9.01e-05 2.768 0.006 7.28e-05 0.000
return_1d_lag3 0.0005 9.03e-05 6.066 0.000 0.000 0.001
return_5d_lag3 9.299e-06 0.000 0.040 0.968 -0.000 0.000
return_10d_lag3 0.0005 4.44e-05 12.279 0.000 0.000 0.001
return_21d_lag3 -0.0001 1.69e-05 -6.699 0.000 -0.000 -8.02e-05
return_1d_lag4 0.0006 9.08e-05 6.278 0.000 0.000 0.001
return_5d_lag4 0.0002 0.000 1.250 0.211 -0.000 0.001
return_10d_lag4 0.0002 3.13e-05 7.472 0.000 0.000 0.000
return_21d_lag4 9.467e-05 1.67e-05 5.653 0.000 6.18e-05 0.000
return_1d_lag5 -1.792e-05 1.89e-05 -0.950 0.342 -5.49e-05 1.9e-05
return_5d_lag5 0.0002 0.000 1.064 0.287 -0.000 0.001
return_10d_lag5 0.0003 3.03e-05 10.693 0.000 0.000 0.000
return_21d_lag5 0.0001 1.67e-05 8.453 0.000 0.000 0.000
year_2014 -0.0004 2.55e-05 -13.817 0.000 -0.000 -0.000
year_2015 -0.0006 2.79e-05 -20.441 0.000 -0.001 -0.001
year_2016 -0.0002 2.73e-05 -9.022 0.000 -0.000 -0.000
year_2017 -0.0002 2.69e-05 -7.333 0.000 -0.000 -0.000
month_2 0.0006 2.2e-05 28.394 0.000 0.001 0.001
month_3 4.204e-05 2.27e-05 1.855 0.064 -2.37e-06 8.65e-05
month_4 0.0002 2.24e-05 8.025 0.000 0.000 0.000
month_5 0.0003 2.21e-05 14.300 0.000 0.000 0.000
month_6 0.0002 2.24e-05 7.894 0.000 0.000 0.000
month_7 0.0002 2.32e-05 8.109 0.000 0.000 0.000
month_8 -6.35e-05 2.34e-05 -2.709 0.007 -0.000 -1.76e-05
month_9 4.795e-05 2.31e-05 2.075 0.038 2.66e-06 9.32e-05
month_10 0.0003 2.37e-05 14.479 0.000 0.000 0.000
month_11 0.0002 2.31e-05 10.264 0.000 0.000 0.000
month_12 -3.354e-05 2.26e-05 -1.485 0.138 -7.78e-05 1.07e-05
capital_goods 0.0010 0.000 8.686 0.000 0.001 0.001
consumer_non-durables 0.0007 0.000 6.399 0.000 0.001 0.001
consumer_services 0.0007 0.000 7.209 0.000 0.001 0.001
energy 0.0003 0.000 2.736 0.006 8.26e-05 0.001
finance 0.0009 0.000 8.318 0.000 0.001 0.001
health_care 0.0006 0.000 6.168 0.000 0.000 0.001
miscellaneous 0.0012 0.000 8.854 0.000 0.001 0.001
public_utilities 0.0002 0.000 1.618 0.106 -4.39e-05 0.000
technology 0.0010 0.000 9.984 0.000 0.001 0.001
transportation 0.0010 0.000 7.928 0.000 0.001 0.001
==============================================================================
Omnibus: 38454.440 Durbin-Watson: 0.233
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1111784.823
Skew: -1.087 Prob(JB): 0.00
Kurtosis: 18.446 Cond. No. 80.7
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Monthly Returns¶
In [200]:
target = 'target_21d'
model = OLS(endog=y[target], exog=add_constant(X))
trained_model = model.fit()
print(trained_model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: target_21d R-squared: 0.059
Model: OLS Adj. R-squared: 0.058
Method: Least Squares F-statistic: 119.7
Date: Sat, 24 Jun 2023 Prob (F-statistic): 0.00
Time: 16:50:17 Log-Likelihood: 4.6155e+05
No. Observations: 109675 AIC: -9.230e+05
Df Residuals: 109617 BIC: -9.224e+05
Df Model: 57
Covariance Type: nonrobust
=========================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------------
const -0.0003 6.44e-05 -4.358 0.000 -0.000 -0.000
dollar_vol_1m -4.008e-05 1.42e-05 -2.820 0.005 -6.79e-05 -1.22e-05
rsi 2.026e-05 3.97e-05 0.511 0.609 -5.75e-05 9.8e-05
bb_high 0.0002 4.65e-05 3.684 0.000 8.01e-05 0.000
bb_low 0.0002 4.45e-05 4.367 0.000 0.000 0.000
atr -0.0002 1.54e-05 -12.330 0.000 -0.000 -0.000
macd -0.0003 4.89e-05 -5.137 0.000 -0.000 -0.000
return_1d 0.0003 6.11e-05 4.853 0.000 0.000 0.000
return_5d -0.0005 0.000 -2.575 0.010 -0.001 -0.000
return_10d -0.0003 0.000 -1.332 0.183 -0.001 0.000
return_21d 0.0018 9.41e-05 18.701 0.000 0.002 0.002
return_42d -0.0018 0.000 -14.314 0.000 -0.002 -0.002
return_63d -0.0012 9.31e-05 -12.676 0.000 -0.001 -0.001
return_1d_lag1 0.0003 6.09e-05 5.014 0.000 0.000 0.000
return_5d_lag1 0.0003 0.000 2.387 0.017 6.01e-05 0.001
return_10d_lag1 9.985e-05 0.000 0.445 0.656 -0.000 0.001
return_21d_lag1 0.0017 7.93e-05 21.339 0.000 0.002 0.002
return_1d_lag2 0.0003 6.09e-05 5.494 0.000 0.000 0.000
return_5d_lag2 7.005e-05 0.000 0.446 0.655 -0.000 0.000
return_10d_lag2 0.0001 0.000 0.716 0.474 -0.000 0.000
return_21d_lag2 0.0005 6.11e-05 7.894 0.000 0.000 0.001
return_1d_lag3 0.0004 6.12e-05 5.956 0.000 0.000 0.000
return_5d_lag3 0.0001 0.000 0.696 0.486 -0.000 0.000
return_10d_lag3 0.0001 3.01e-05 4.171 0.000 6.65e-05 0.000
return_21d_lag3 -8.503e-05 1.15e-05 -7.415 0.000 -0.000 -6.26e-05
return_1d_lag4 0.0004 6.16e-05 6.187 0.000 0.000 0.001
return_5d_lag4 5.046e-05 0.000 0.401 0.688 -0.000 0.000
return_10d_lag4 3.895e-05 2.12e-05 1.838 0.066 -2.59e-06 8.05e-05
return_21d_lag4 0.0001 1.14e-05 13.100 0.000 0.000 0.000
return_1d_lag5 3.351e-06 1.28e-05 0.262 0.793 -2.17e-05 2.84e-05
return_5d_lag5 -9.804e-06 0.000 -0.080 0.936 -0.000 0.000
return_10d_lag5 6.256e-06 2.05e-05 0.305 0.761 -3.4e-05 4.65e-05
return_21d_lag5 -3.545e-05 1.13e-05 -3.134 0.002 -5.76e-05 -1.33e-05
year_2014 -0.0003 1.73e-05 -17.088 0.000 -0.000 -0.000
year_2015 -0.0006 1.89e-05 -29.827 0.000 -0.001 -0.001
year_2016 -0.0002 1.85e-05 -9.707 0.000 -0.000 -0.000
year_2017 -0.0002 1.82e-05 -10.028 0.000 -0.000 -0.000
month_2 8.855e-05 1.49e-05 5.950 0.000 5.94e-05 0.000
month_3 -0.0001 1.54e-05 -9.594 0.000 -0.000 -0.000
month_4 -0.0001 1.52e-05 -7.960 0.000 -0.000 -9.11e-05
month_5 2.356e-05 1.5e-05 1.574 0.116 -5.78e-06 5.29e-05
month_6 -1.92e-05 1.52e-05 -1.266 0.206 -4.89e-05 1.05e-05
month_7 -0.0003 1.57e-05 -18.369 0.000 -0.000 -0.000
month_8 -0.0002 1.59e-05 -14.877 0.000 -0.000 -0.000
month_9 -0.0002 1.57e-05 -11.661 0.000 -0.000 -0.000
month_10 6.676e-05 1.61e-05 4.147 0.000 3.52e-05 9.83e-05
month_11 -0.0001 1.57e-05 -7.393 0.000 -0.000 -8.52e-05
month_12 -0.0004 1.53e-05 -25.897 0.000 -0.000 -0.000
capital_goods 0.0008 7.57e-05 11.198 0.000 0.001 0.001
consumer_non-durables 0.0007 7.84e-05 8.618 0.000 0.001 0.001
consumer_services 0.0006 7.01e-05 8.933 0.000 0.000 0.001
energy 0.0002 7.22e-05 2.652 0.008 4.99e-05 0.000
finance 0.0008 7.37e-05 10.794 0.000 0.001 0.001
health_care 0.0005 6.89e-05 7.493 0.000 0.000 0.001
miscellaneous 0.0012 9.02e-05 12.969 0.000 0.001 0.001
public_utilities 0.0001 8.71e-05 1.241 0.215 -6.26e-05 0.000
technology 0.0009 6.95e-05 13.417 0.000 0.001 0.001
transportation 0.0009 8.24e-05 11.165 0.000 0.001 0.001
==============================================================================
Omnibus: 40182.050 Durbin-Watson: 0.129
Prob(Omnibus): 0.000 Jarque-Bera (JB): 848232.085
Skew: -1.254 Prob(JB): 0.00
Kurtosis: 16.391 Cond. No. 80.7
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
사이킷런을 이용한 선형 OLS 회귀¶
- 05_predicting_stock_returns_with_linear_regression.ipynb
Prediction stock returns with linear regression¶
Imports & Settings¶
In [201]:
import warnings
warnings.filterwarnings('ignore')
In [202]:
%matplotlib inline
from time import time
from pathlib import Path
import pandas as pd
import numpy as np
from scipy.stats import spearmanr
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.pipeline import Pipeline
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
In [203]:
sns.set_style('darkgrid')
idx = pd.IndexSlice
In [204]:
YEAR = 252
Load Data¶
In [205]:
with pd.HDFStore('/content/data.h5') as store:
data = (store['model_data']
.dropna()
.drop(['open', 'close', 'low', 'high'], axis=1))
In [206]:
data.index.names = ['symbol', 'date']
In [207]:
data = data.drop([c for c in data.columns if 'lag' in c], axis=1)
Select Investment Universe¶
In [208]:
data = data[data.dollar_vol_rank<100]
In [209]:
data.info(null_counts=True)
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 109675 entries, ('AAL', Timestamp('2013-07-25 00:00:00')) to ('ZTS', Timestamp('2014-12-04 00:00:00'))
Data columns (total 45 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 volume 109675 non-null float64
1 dollar_vol 109675 non-null float64
2 dollar_vol_1m 109675 non-null float64
3 dollar_vol_rank 109675 non-null float64
4 rsi 109675 non-null float64
5 bb_high 109675 non-null float64
6 bb_low 109675 non-null float64
7 atr 109675 non-null float64
8 macd 109675 non-null float64
9 return_1d 109675 non-null float64
10 return_5d 109675 non-null float64
11 return_10d 109675 non-null float64
12 return_21d 109675 non-null float64
13 return_42d 109675 non-null float64
14 return_63d 109675 non-null float64
15 target_1d 109675 non-null float64
16 target_5d 109675 non-null float64
17 target_10d 109675 non-null float64
18 target_21d 109675 non-null float64
19 year_2014 109675 non-null uint8
20 year_2015 109675 non-null uint8
21 year_2016 109675 non-null uint8
22 year_2017 109675 non-null uint8
23 month_2 109675 non-null uint8
24 month_3 109675 non-null uint8
25 month_4 109675 non-null uint8
26 month_5 109675 non-null uint8
27 month_6 109675 non-null uint8
28 month_7 109675 non-null uint8
29 month_8 109675 non-null uint8
30 month_9 109675 non-null uint8
31 month_10 109675 non-null uint8
32 month_11 109675 non-null uint8
33 month_12 109675 non-null uint8
34 capital_goods 109675 non-null uint8
35 consumer_durables 109675 non-null uint8
36 consumer_non-durables 109675 non-null uint8
37 consumer_services 109675 non-null uint8
38 energy 109675 non-null uint8
39 finance 109675 non-null uint8
40 health_care 109675 non-null uint8
41 miscellaneous 109675 non-null uint8
42 public_utilities 109675 non-null uint8
43 technology 109675 non-null uint8
44 transportation 109675 non-null uint8
dtypes: float64(19), uint8(26)
memory usage: 19.8+ MB
Create Model Data¶
In [210]:
y = data.filter(like='target')
X = data.drop(y.columns, axis=1)
X = X.drop(['dollar_vol', 'dollar_vol_rank', 'volume', 'consumer_durables'], axis=1)
Custom MultipleTimeSeriesCV¶
In [211]:
class MultipleTimeSeriesCV:
"""Generates tuples of train_idx, test_idx pairs
Assumes the MultiIndex contains levels 'symbol' and 'date'
purges overlapping outcomes"""
def __init__(self,
n_splits=3,
train_period_length=126,
test_period_length=21,
lookahead=None,
shuffle=False):
self.n_splits = n_splits
self.lookahead = lookahead
self.test_length = test_period_length
self.train_length = train_period_length
self.shuffle = shuffle
def split(self, X, y=None, groups=None):
unique_dates = X.index.get_level_values('date').unique()
days = sorted(unique_dates, reverse=True)
split_idx = []
for i in range(self.n_splits):
test_end_idx = i * self.test_length
test_start_idx = test_end_idx + self.test_length
train_end_idx = test_start_idx + + self.lookahead - 1
train_start_idx = train_end_idx + self.train_length + self.lookahead - 1
split_idx.append([train_start_idx, train_end_idx,
test_start_idx, test_end_idx])
dates = X.reset_index()[['date']]
for train_start, train_end, test_start, test_end in split_idx:
train_idx = dates[(dates.date > days[train_start])
& (dates.date <= days[train_end])].index
test_idx = dates[(dates.date > days[test_start])
& (dates.date <= days[test_end])].index
if self.shuffle:
np.random.shuffle(list(train_idx))
yield train_idx, test_idx
def get_n_splits(self, X, y, groups=None):
return self.n_splits
Verify that it works¶
In [212]:
train_period_length = 63
test_period_length = 10
n_splits = int(3 * YEAR/test_period_length)
lookahead =1
cv = MultipleTimeSeriesCV(n_splits=n_splits,
test_period_length=test_period_length,
lookahead=lookahead,
train_period_length=train_period_length)
In [213]:
i = 0
for train_idx, test_idx in cv.split(X=data):
train = data.iloc[train_idx]
train_dates = train.index.get_level_values('date')
test = data.iloc[test_idx]
test_dates = test.index.get_level_values('date')
df = train.reset_index().append(test.reset_index())
n = len(df)
assert n== len(df.drop_duplicates())
print(train.groupby(level='symbol').size().value_counts().index[0],
train_dates.min().date(), train_dates.max().date(),
test.groupby(level='symbol').size().value_counts().index[0],
test_dates.min().date(), test_dates.max().date())
i += 1
if i == 10:
break
63 2017-08-16 2017-11-14 10 2017-11-15 2017-11-29 63 2017-08-02 2017-10-30 10 2017-10-31 2017-11-14 63 2017-07-19 2017-10-16 10 2017-10-17 2017-10-30 63 2017-07-05 2017-10-02 10 2017-10-03 2017-10-16 63 2017-06-20 2017-09-18 10 2017-09-19 2017-10-02 63 2017-06-06 2017-09-01 10 2017-09-05 2017-09-18 63 2017-05-22 2017-08-18 10 2017-08-21 2017-09-01 63 2017-05-08 2017-08-04 10 2017-08-07 2017-08-18 63 2017-04-24 2017-07-21 10 2017-07-24 2017-08-04 62 2017-04-10 2017-07-07 10 2017-07-10 2017-07-21
Visualization helper functions¶
Prediction vs Actual Scatter Plot¶
In [214]:
def plot_preds_scatter(df, ticker=None):
if ticker is not None:
idx = pd.IndexSlice
df = df.loc[idx[ticker, :], :]
j = sns.jointplot(x='predicted', y='actuals',
robust=True, ci=None,
line_kws={'lw': 1, 'color': 'k'},
scatter_kws={'s': 1},
data=df,
kind='reg')
j.ax_joint.yaxis.set_major_formatter(
FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
j.ax_joint.xaxis.set_major_formatter(
FuncFormatter(lambda x, _: '{:.1%}'.format(x)))
j.ax_joint.set_xlabel('Predicted')
j.ax_joint.set_ylabel('Actuals')
Daily IC Distribution¶
In [215]:
def plot_ic_distribution(df, ax=None):
if ax is not None:
sns.distplot(df.ic, ax=ax)
else:
ax = sns.distplot(df.ic)
mean, median = df.ic.mean(), df.ic.median()
ax.axvline(0, lw=1, ls='--', c='k')
ax.text(x=.05, y=.9,
s=f'Mean: {mean:8.2f}\nMedian: {median:5.2f}',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel('Information Coefficient')
sns.despine()
plt.tight_layout()
Rolling Daily IC¶
In [216]:
def plot_rolling_ic(df):
fig, axes = plt.subplots(nrows=2, sharex=True, figsize=(14, 8))
rolling_result = df.sort_index().rolling(21).mean().dropna()
mean_ic = df.ic.mean()
rolling_result.ic.plot(ax=axes[0],
title=f'Information Coefficient (Mean: {mean_ic:.2f})',
lw=1)
axes[0].axhline(0, lw=.5, ls='-', color='k')
axes[0].axhline(mean_ic, lw=1, ls='--', color='k')
mean_rmse = df.rmse.mean()
rolling_result.rmse.plot(ax=axes[1],
title=f'Root Mean Squared Error (Mean: {mean_rmse:.2%})',
lw=1,
ylim=(0, df.rmse.max()))
axes[1].axhline(df.rmse.mean(), lw=1, ls='--', color='k')
sns.despine()
plt.tight_layout()
Linear Regression with sklearn¶
Set up cross-validation¶
In [217]:
train_period_length = 63
test_period_length = 10
n_splits = int(3 * YEAR / test_period_length)
lookahead = 1
cv = MultipleTimeSeriesCV(n_splits=n_splits,
test_period_length=test_period_length,
lookahead=lookahead,
train_period_length=train_period_length)
Run cross-validation with LinearRegression¶
In [218]:
%%time
target = f'target_{lookahead}d'
lr_predictions, lr_scores = [], []
lr = LinearRegression()
for i, (train_idx, test_idx) in enumerate(cv.split(X), 1):
X_train, y_train, = X.iloc[train_idx], y[target].iloc[train_idx]
X_test, y_test = X.iloc[test_idx], y[target].iloc[test_idx]
lr.fit(X=X_train, y=y_train)
y_pred = lr.predict(X_test)
preds = y_test.to_frame('actuals').assign(predicted=y_pred)
preds_by_day = preds.groupby(level='date')
scores = pd.concat([preds_by_day.apply(lambda x: spearmanr(x.predicted,
x.actuals)[0] * 100)
.to_frame('ic'),
preds_by_day.apply(lambda x: np.sqrt(mean_squared_error(y_pred=x.predicted,
y_true=x.actuals)))
.to_frame('rmse')], axis=1)
lr_scores.append(scores)
lr_predictions.append(preds)
lr_scores = pd.concat(lr_scores)
lr_predictions = pd.concat(lr_predictions)
CPU times: user 5.8 s, sys: 6.57 s, total: 12.4 s Wall time: 8.9 s
Persist results¶
In [219]:
lr_scores.to_hdf('/content/data.h5', 'lr/scores')
lr_predictions.to_hdf('/content/data.h5', 'lr/predictions')
In [220]:
lr_scores = pd.read_hdf('/content/data.h5', 'lr/scores')
lr_predictions = pd.read_hdf('/content/data.h5', 'lr/predictions')
Evaluate results¶
In [221]:
lr_r, lr_p = spearmanr(lr_predictions.actuals, lr_predictions.predicted)
print(f'Information Coefficient (overall): {lr_r:.3%} (p-value: {lr_p:.4%})')
Information Coefficient (overall): 1.531% (p-value: 0.0031%)
Prediction vs Actuals Scatter¶
In [222]:
plot_preds_scatter(lr_predictions)
Daily IC Distribution¶
In [223]:
plot_ic_distribution(lr_scores)
Rolling Daily IC¶
In [224]:
plot_rolling_ic(lr_scores)
Ridge Regression¶
Define cross-validation parameters¶
In [225]:
ridge_alphas = np.logspace(-4, 4, 9)
ridge_alphas = sorted(list(ridge_alphas) + list(ridge_alphas * 5))
In [226]:
n_splits = int(3 * YEAR/test_period_length)
train_period_length = 63
test_period_length = 10
lookahead = 1
cv = MultipleTimeSeriesCV(n_splits=n_splits,
test_period_length=test_period_length,
lookahead=lookahead,
train_period_length=train_period_length)
Run cross-validation¶
In [227]:
target = f'target_{lookahead}d'
X = X.drop([c for c in X.columns if 'year' in c], axis=1)
In [228]:
%%time
ridge_coeffs, ridge_scores, ridge_predictions = {}, [], []
for alpha in ridge_alphas:
print(alpha, end=' ', flush=True)
start = time()
model = Ridge(alpha=alpha,
fit_intercept=False,
random_state=42)
pipe = Pipeline([
('scaler', StandardScaler()),
('model', model)])
coeffs = []
for i, (train_idx, test_idx) in enumerate(cv.split(X), 1):
X_train, y_train, = X.iloc[train_idx], y[target].iloc[train_idx]
X_test, y_test = X.iloc[test_idx], y[target].iloc[test_idx]
pipe.fit(X=X_train, y=y_train)
y_pred = pipe.predict(X_test)
preds = y_test.to_frame('actuals').assign(predicted=y_pred)
preds_by_day = preds.groupby(level='date')
scores = pd.concat([preds_by_day.apply(lambda x: spearmanr(x.predicted,
x.actuals)[0] * 100)
.to_frame('ic'),
preds_by_day.apply(lambda x: np.sqrt(mean_squared_error(y_pred=x.predicted,
y_true=x.actuals)))
.to_frame('rmse')], axis=1)
ridge_scores.append(scores.assign(alpha=alpha))
ridge_predictions.append(preds.assign(alpha=alpha))
coeffs.append(pipe.named_steps['model'].coef_)
ridge_coeffs[alpha] = np.mean(coeffs, axis=0)
print('\n')
0.0001 0.0005 0.001 0.005 0.01 0.05 0.1 0.5 1.0 5.0 10.0 50.0 100.0 500.0 1000.0 5000.0 10000.0 50000.0 CPU times: user 1min 23s, sys: 1min, total: 2min 23s Wall time: 1min 45s
Persist results¶
In [229]:
ridge_scores = pd.concat(ridge_scores)
ridge_scores.to_hdf('/content/data.h5', 'ridge/scores')
ridge_coeffs = pd.DataFrame(ridge_coeffs, index=X.columns).T
ridge_coeffs.to_hdf('/content/data.h5', 'ridge/coeffs')
ridge_predictions = pd.concat(ridge_predictions)
ridge_predictions.to_hdf('/content/data.h5', 'ridge/predictions')
In [230]:
ridge_scores = pd.read_hdf('/content/data.h5', 'ridge/scores')
ridge_coeffs = pd.read_hdf('/content/data.h5', 'ridge/coeffs')
ridge_predictions = pd.read_hdf('/content/data.h5', 'ridge/predictions')
Evaluate Ridge Results¶
In [231]:
ridge_r, ridge_p = spearmanr(ridge_predictions.actuals, ridge_predictions.predicted)
print(f'Information Coefficient (overall): {ridge_r:.3%} (p-value: {ridge_p:.4%})')
Information Coefficient (overall): 1.551% (p-value: 0.0000%)
In [232]:
ridge_scores.groupby('alpha').ic.describe()
Out[232]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| alpha | ||||||||
| 0.00 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.00 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.00 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.01 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.01 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.05 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.10 | 750.00 | 1.86 | 18.57 | -56.79 | -10.01 | 1.98 | 14.10 | 53.02 |
| 0.50 | 750.00 | 1.86 | 18.57 | -56.84 | -10.00 | 1.97 | 14.12 | 53.03 |
| 1.00 | 750.00 | 1.86 | 18.57 | -56.84 | -10.00 | 1.97 | 14.11 | 53.16 |
| 5.00 | 750.00 | 1.87 | 18.56 | -56.77 | -10.12 | 1.96 | 14.09 | 53.15 |
| 10.00 | 750.00 | 1.87 | 18.57 | -56.96 | -10.08 | 1.95 | 14.07 | 53.02 |
| 50.00 | 750.00 | 1.90 | 18.58 | -57.42 | -9.95 | 1.94 | 14.09 | 53.24 |
| 100.00 | 750.00 | 1.93 | 18.60 | -57.45 | -10.31 | 1.97 | 14.05 | 53.71 |
| 500.00 | 750.00 | 1.86 | 18.73 | -57.34 | -10.48 | 1.62 | 13.91 | 54.71 |
| 1,000.00 | 750.00 | 1.73 | 18.90 | -57.71 | -10.91 | 1.25 | 14.05 | 56.91 |
| 5,000.00 | 750.00 | 1.37 | 19.33 | -55.58 | -11.60 | 0.72 | 14.79 | 62.55 |
| 10,000.00 | 750.00 | 1.20 | 19.51 | -67.86 | -11.75 | 0.72 | 14.95 | 62.33 |
| 50,000.00 | 750.00 | 0.83 | 19.57 | -54.99 | -12.07 | 0.07 | 14.76 | 55.63 |
In [233]:
fig, axes = plt.subplots(ncols=2, sharex=True, figsize=(15, 5))
scores_by_alpha = ridge_scores.groupby('alpha').ic.agg(['mean', 'median'])
best_alpha_mean = scores_by_alpha['mean'].idxmax()
best_alpha_median = scores_by_alpha['median'].idxmax()
ax = sns.lineplot(x='alpha',
y='ic',
data=ridge_scores,
estimator=np.mean,
label='Mean',
ax=axes[0])
scores_by_alpha['median'].plot(logx=True,
ax=axes[0],
label='Median')
axes[0].axvline(best_alpha_mean,
ls='--',
c='k',
lw=1,
label='Max. Mean')
axes[0].axvline(best_alpha_median,
ls='-.',
c='k',
lw=1,
label='Max. Median')
axes[0].legend()
axes[0].set_xscale('log')
axes[0].set_xlabel('Alpha')
axes[0].set_ylabel('Information Coefficient')
axes[0].set_title('Cross Validation Performance')
ridge_coeffs.plot(logx=True,
legend=False,
ax=axes[1],
title='Ridge Coefficient Path')
axes[1].axvline(best_alpha_mean,
ls='--',
c='k',
lw=1,
label='Max. Mean')
axes[1].axvline(best_alpha_median,
ls='-.',
c='k',
lw=1,
label='Max. Median')
axes[1].set_xlabel('Alpha')
axes[1].set_ylabel('Coefficient Value')
fig.suptitle('Ridge Results', fontsize=14)
sns.despine()
fig.tight_layout()
fig.subplots_adjust(top=.9)
In [234]:
best_alpha = ridge_scores.groupby('alpha').ic.mean().idxmax()
fig, axes = plt.subplots(ncols=2, figsize=(15, 5))
plot_ic_distribution(ridge_scores[ridge_scores.alpha == best_alpha],
ax=axes[0])
axes[0].set_title('Daily Information Coefficients')
top_coeffs = ridge_coeffs.loc[best_alpha].abs().sort_values().head(10).index
top_coeffs.tolist()
ridge_coeffs.loc[best_alpha, top_coeffs].sort_values().plot.barh(ax=axes[1],
title='Top 10 Coefficients')
sns.despine()
fig.tight_layout()
In [235]:
plot_rolling_ic(ridge_scores[ridge_scores.alpha==best_alpha])
Lasso CV¶
Define cross-validation parameters¶
In [236]:
lasso_alphas = np.logspace(-10, -3, 8)
In [237]:
train_period_length = 63
test_period_length = 10
YEAR = 252
n_splits = int(3 * YEAR / test_period_length) # three years
lookahead = 1
In [238]:
cv = MultipleTimeSeriesCV(n_splits=n_splits,
test_period_length=test_period_length,
lookahead=lookahead,
train_period_length=train_period_length)
Run cross-validation with Lasso regression¶
In [239]:
target = f'target_{lookahead}d'
scaler = StandardScaler()
X = X.drop([c for c in X.columns if 'year' in c], axis=1)
In [240]:
%%time
lasso_coeffs, lasso_scores, lasso_predictions = {}, [], []
for alpha in lasso_alphas:
print(alpha, end=' ', flush=True)
model = Lasso(alpha=alpha,
fit_intercept=False, # StandardScaler centers data
random_state=42,
tol=1e-3,
max_iter=1000,
warm_start=True,
selection='random')
pipe = Pipeline([
('scaler', StandardScaler()),
('model', model)])
coeffs = []
for i, (train_idx, test_idx) in enumerate(cv.split(X), 1):
t = time()
X_train, y_train, = X.iloc[train_idx], y[target].iloc[train_idx]
X_test, y_test = X.iloc[test_idx], y[target].iloc[test_idx]
pipe.fit(X=X_train, y=y_train)
y_pred = pipe.predict(X_test)
preds = y_test.to_frame('actuals').assign(predicted=y_pred)
preds_by_day = preds.groupby(level='date')
scores = pd.concat([preds_by_day.apply(lambda x: spearmanr(x.predicted,
x.actuals)[0] * 100)
.to_frame('ic'),
preds_by_day.apply(lambda x: np.sqrt(mean_squared_error(y_pred=x.predicted,
y_true=x.actuals)))
.to_frame('rmse')],
axis=1)
lasso_scores.append(scores.assign(alpha=alpha))
lasso_predictions.append(preds.assign(alpha=alpha))
coeffs.append(pipe.named_steps['model'].coef_)
lasso_coeffs[alpha] = np.mean(coeffs, axis=0)
1e-10 1e-09 1e-08 1e-07 1e-06 1e-05 0.0001 0.001 CPU times: user 2min 6s, sys: 1min 51s, total: 3min 58s Wall time: 3min 7s
Persist results¶
In [243]:
lasso_scores = pd.concat(lasso_scores)
lasso_scores.to_hdf('/content/data.h5', 'lasso/scores')
lasso_coeffs = pd.DataFrame(lasso_coeffs, index=X.columns).T
lasso_coeffs.to_hdf('/content/data.h5', 'lasso/coeffs')
lasso_predictions = pd.concat(lasso_predictions)
lasso_predictions.to_hdf('/content/data.h5', 'lasso/predictions')
Evaluate Lasso Results¶
In [244]:
best_alpha = lasso_scores.groupby('alpha').ic.mean().idxmax()
preds = lasso_predictions[lasso_predictions.alpha==best_alpha]
lasso_r, lasso_p = spearmanr(preds.actuals, preds.predicted)
print(f'Information Coefficient (overall): {lasso_r:.3%} (p-value: {lasso_p:.4%})')
Information Coefficient (overall): 3.595% (p-value: 0.0000%)
In [245]:
lasso_scores.groupby('alpha').ic.agg(['mean', 'median'])
Out[245]:
| mean | median | |
|---|---|---|
| alpha | ||
| 0.00 | 1.86 | 1.98 |
| 0.00 | 1.86 | 1.98 |
| 0.00 | 1.86 | 1.98 |
| 0.00 | 1.87 | 1.97 |
| 0.00 | 1.88 | 1.96 |
| 0.00 | 1.94 | 2.19 |
| 0.00 | 1.58 | 1.01 |
| 0.00 | 1.03 | 1.77 |
Lasso Coefficient Path¶
In [246]:
fig, axes = plt.subplots(ncols=2, sharex=True, figsize=(15, 5))
scores_by_alpha = lasso_scores.groupby('alpha').ic.agg(['mean', 'median'])
best_alpha_mean = scores_by_alpha['mean'].idxmax()
best_alpha_median = scores_by_alpha['median'].idxmax()
ax = sns.lineplot(x='alpha', y='ic', data=lasso_scores, estimator=np.mean, label='Mean', ax=axes[0])
scores_by_alpha['median'].plot(logx=True, ax=axes[0], label='Median')
axes[0].axvline(best_alpha_mean, ls='--', c='k', lw=1, label='Max. Mean')
axes[0].axvline(best_alpha_median, ls='-.', c='k', lw=1, label='Max. Median')
axes[0].legend()
axes[0].set_xscale('log')
axes[0].set_xlabel('Alpha')
axes[0].set_ylabel('Information Coefficient')
axes[0].set_title('Cross Validation Performance')
lasso_coeffs.plot(logx=True, legend=False, ax=axes[1], title='Lasso Coefficient Path')
axes[1].axvline(best_alpha_mean, ls='--', c='k', lw=1, label='Max. Mean')
axes[1].axvline(best_alpha_median, ls='-.', c='k', lw=1, label='Max. Median')
axes[1].set_xlabel('Alpha')
axes[1].set_ylabel('Coefficient Value')
fig.suptitle('Lasso Results', fontsize=14)
fig.tight_layout()
fig.subplots_adjust(top=.9)
sns.despine();
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-246-ca48843bf9be> in <cell line: 7>() 5 best_alpha_median = scores_by_alpha['median'].idxmax() 6 ----> 7 ax = sns.lineplot(x='alpha', y='ic', data=lasso_scores, estimator=np.mean, label='Mean', ax=axes[0]) 8 9 scores_by_alpha['median'].plot(logx=True, ax=axes[0], label='Median') /usr/local/lib/python3.10/dist-packages/seaborn/relational.py in lineplot(data, x, y, hue, size, style, units, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, estimator, errorbar, n_boot, seed, orient, sort, err_style, err_kws, legend, ci, ax, **kwargs) 643 kwargs["color"] = _default_color(ax.plot, hue, color, kwargs) 644 --> 645 p.plot(ax, kwargs) 646 return ax 647 /usr/local/lib/python3.10/dist-packages/seaborn/relational.py in plot(self, ax, kws) 421 # Loop over the semantic subsets and add to the plot 422 grouping_vars = "hue", "size", "style" --> 423 for sub_vars, sub_data in self.iter_data(grouping_vars, from_comp_data=True): 424 425 if self.sort: /usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py in iter_data(self, grouping_vars, reverse, from_comp_data, by_facet, allow_empty, dropna) 1026 1027 if from_comp_data: -> 1028 data = self.comp_data 1029 else: 1030 data = self.plot_data /usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py in comp_data(self) 1132 else: 1133 comp_col = pd.Series(dtype=float, name=var) -> 1134 comp_data.insert(0, var, comp_col) 1135 1136 self._comp_data = comp_data /usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in insert(self, loc, column, value, allow_duplicates) 4819 raise TypeError("loc must be int") 4820 -> 4821 value = self._sanitize_column(value) 4822 self._mgr.insert(loc, column, value) 4823 /usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in _sanitize_column(self, value) 4910 return _reindex_for_setitem(value, self.index) 4911 elif is_dict_like(value): -> 4912 return _reindex_for_setitem(Series(value), self.index) 4913 4914 if is_list_like(value): /usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in _reindex_for_setitem(value, index) 12023 if not value.index.is_unique: 12024 # duplicate axis > 12025 raise err 12026 12027 raise TypeError( /usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in _reindex_for_setitem(value, index) 12018 # GH#4107 12019 try: > 12020 reindexed_value = value.reindex(index)._values 12021 except ValueError as err: 12022 # raised in MultiIndex.from_tuples, see test_insert_error_msmgs /usr/local/lib/python3.10/dist-packages/pandas/core/series.py in reindex(self, *args, **kwargs) 5092 ) 5093 kwargs.update({"index": index}) -> 5094 return super().reindex(**kwargs) 5095 5096 @overload /usr/local/lib/python3.10/dist-packages/pandas/core/generic.py in reindex(self, *args, **kwargs) 5287 5288 # perform the reindex on the axes -> 5289 return self._reindex_axes( 5290 axes, level, limit, tolerance, method, fill_value, copy 5291 ).__finalize__(self, method="reindex") /usr/local/lib/python3.10/dist-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy) 5307 5308 axis = self._get_axis_number(a) -> 5309 obj = obj._reindex_with_indexers( 5310 {axis: [new_index, indexer]}, 5311 fill_value=fill_value, /usr/local/lib/python3.10/dist-packages/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups) 5353 5354 # TODO: speed up on homogeneous DataFrame objects (see _reindex_multi) -> 5355 new_data = new_data.reindex_indexer( 5356 index, 5357 indexer, /usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy) 735 # some axes don't allow reindexing with dups 736 if not allow_dups: --> 737 self.axes[axis]._validate_can_reindex(indexer) 738 739 if axis >= self.ndim: /usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer) 4314 # trying to reindex on an axis with duplicates 4315 if not self._index_as_unique and len(indexer): -> 4316 raise ValueError("cannot reindex on an axis with duplicate labels") 4317 4318 def reindex( ValueError: cannot reindex on an axis with duplicate labels
Lasso IC Distribution and Top 10 Features¶
In [247]:
best_alpha = lasso_scores.groupby('alpha').ic.mean().idxmax()
fig, axes = plt.subplots(ncols=2, figsize=(15, 5))
plot_ic_distribution(lasso_scores[lasso_scores.alpha==best_alpha], ax=axes[0])
axes[0].set_title('Daily Information Coefficients')
top_coeffs = lasso_coeffs.loc[best_alpha].abs().sort_values().head(10).index
top_coeffs.tolist()
lasso_coeffs.loc[best_alpha, top_coeffs].sort_values().plot.barh(ax=axes[1], title='Top 10 Coefficients')
sns.despine()
fig.tight_layout();
Compare results¶
In [248]:
best_ridge_alpha = ridge_scores.groupby('alpha').ic.mean().idxmax()
best_ridge_preds = ridge_predictions[ridge_predictions.alpha==best_ridge_alpha]
best_ridge_scores = ridge_scores[ridge_scores.alpha==best_ridge_alpha]
In [249]:
best_lasso_alpha = lasso_scores.groupby('alpha').ic.mean().idxmax()
best_lasso_preds = lasso_predictions[lasso_predictions.alpha==best_lasso_alpha]
best_lasso_scores = lasso_scores[lasso_scores.alpha==best_lasso_alpha]
In [250]:
df = pd.concat([lr_scores.assign(Model='Linear Regression'),
best_ridge_scores.assign(Model='Ridge Regression'),
best_lasso_scores.assign(Model='Lasso Regression')]).drop('alpha', axis=1)
df.columns = ['IC', 'RMSE', 'Model']
In [251]:
scores = df.groupby('Model').IC.agg(['mean', 'median'])
fig, axes = plt.subplots(ncols=2, figsize=(14,4), sharey=True, sharex=True)
scores['mean'].plot.barh(ax=axes[0], xlim=(1.85, 2), title='Mean')
scores['median'].plot.barh(ax=axes[1], xlim=(1.8, 2.1), title='Median')
axes[0].set_xlabel('Daily IC')
axes[1].set_xlabel('Daily IC')
fig.suptitle('Daily Information Coefficient by Model', fontsize=14)
sns.despine()
fig.tight_layout()
fig.subplots_adjust(top=.9)
예측 신호의 품질 비교¶
- 06_evaluating_signals_using_alphalens.ipynb
- alphalens 설치 불가
선형 분류¶
로지스틱 회귀 모델¶
- 목표 함수
- 로지스틱 함수
- 최대 우도 측정(MLE)
statsmodels로 추론을 수행하는 방법¶
- 07_logistic_regression_macro_data.ipynb
Logistic Regression with Macro Data¶
In [253]:
%matplotlib inline
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
In [254]:
sns.set_style('whitegrid')
Data Set¶
| Variable | Description | Transformation |
|---|---|---|
| realgdp | Real gross domestic product | Annual Growth Rate |
| realcons | Real personal consumption expenditures | Annual Growth Rate |
| realinv | Real gross private domestic investment | Annual Growth Rate |
| realgovt | Real federal expenditures & gross investment | Annual Growth Rate |
| realdpi | Real private disposable income | Annual Growth Rate |
| m1 | M1 nominal money stock | Annual Growth Rate |
| tbilrate | Monthly treasury bill rate | Level |
| unemp | Seasonally adjusted unemployment rate (%) | Level |
| infl | Inflation rate | Level |
| realint | Real interest rate | Level |
In [255]:
data = pd.DataFrame(sm.datasets.macrodata.load().data)
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 203 entries, 0 to 202 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 year 203 non-null float64 1 quarter 203 non-null float64 2 realgdp 203 non-null float64 3 realcons 203 non-null float64 4 realinv 203 non-null float64 5 realgovt 203 non-null float64 6 realdpi 203 non-null float64 7 cpi 203 non-null float64 8 m1 203 non-null float64 9 tbilrate 203 non-null float64 10 unemp 203 non-null float64 11 pop 203 non-null float64 12 infl 203 non-null float64 13 realint 203 non-null float64 dtypes: float64(14) memory usage: 22.3 KB
In [256]:
data.head()
Out[256]:
| year | quarter | realgdp | realcons | realinv | realgovt | realdpi | cpi | m1 | tbilrate | unemp | pop | infl | realint | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1,959.00 | 1.00 | 2,710.35 | 1,707.40 | 286.90 | 470.05 | 1,886.90 | 28.98 | 139.70 | 2.82 | 5.80 | 177.15 | 0.00 | 0.00 |
| 1 | 1,959.00 | 2.00 | 2,778.80 | 1,733.70 | 310.86 | 481.30 | 1,919.70 | 29.15 | 141.70 | 3.08 | 5.10 | 177.83 | 2.34 | 0.74 |
| 2 | 1,959.00 | 3.00 | 2,775.49 | 1,751.80 | 289.23 | 491.26 | 1,916.40 | 29.35 | 140.50 | 3.82 | 5.30 | 178.66 | 2.74 | 1.09 |
| 3 | 1,959.00 | 4.00 | 2,785.20 | 1,753.70 | 299.36 | 484.05 | 1,931.30 | 29.37 | 140.00 | 4.33 | 5.60 | 179.39 | 0.27 | 4.06 |
| 4 | 1,960.00 | 1.00 | 2,847.70 | 1,770.50 | 331.72 | 462.20 | 1,955.50 | 29.54 | 139.60 | 3.50 | 5.20 | 180.01 | 2.31 | 1.19 |
Data Prep¶
To obtain a binary target variable, we compute the 20-quarter rolling average of the annual growth rate of quarterly real GDP. We then assign 1 if current growth exceeds the moving average and 0 otherwise. Finally, we shift the indicator variables to align next quarter's outcome with the current quarter.
In [257]:
data['growth_rate'] = data.realgdp.pct_change(4)
data['target'] = (data.growth_rate > data.growth_rate.rolling(20).mean()).astype(int).shift(-1)
data.quarter = data.quarter.astype(int)
In [258]:
data.target.value_counts()
Out[258]:
0.00 112 1.00 90 Name: target, dtype: int64
In [259]:
data.tail()
Out[259]:
| year | quarter | realgdp | realcons | realinv | realgovt | realdpi | cpi | m1 | tbilrate | unemp | pop | infl | realint | growth_rate | target | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 198 | 2,008.00 | 3 | 13,324.60 | 9,267.70 | 1,990.69 | 991.55 | 9,838.30 | 216.89 | 1,474.70 | 1.17 | 6.00 | 305.27 | -3.16 | 4.33 | 0.00 | 0.00 |
| 199 | 2,008.00 | 4 | 13,141.92 | 9,195.30 | 1,857.66 | 1,007.27 | 9,920.40 | 212.17 | 1,576.50 | 0.12 | 6.90 | 305.95 | -8.79 | 8.91 | -0.02 | 0.00 |
| 200 | 2,009.00 | 1 | 12,925.41 | 9,209.20 | 1,558.49 | 996.29 | 9,926.40 | 212.67 | 1,592.80 | 0.22 | 8.10 | 306.55 | 0.94 | -0.71 | -0.03 | 0.00 |
| 201 | 2,009.00 | 2 | 12,901.50 | 9,189.00 | 1,456.68 | 1,023.53 | 10,077.50 | 214.47 | 1,653.60 | 0.18 | 9.20 | 307.23 | 3.37 | -3.19 | -0.04 | 0.00 |
| 202 | 2,009.00 | 3 | 12,990.34 | 9,256.00 | 1,486.40 | 1,044.09 | 10,040.60 | 216.38 | 1,673.90 | 0.12 | 9.60 | 308.01 | 3.56 | -3.44 | -0.03 | NaN |
In [260]:
pct_cols = ['realcons', 'realinv', 'realgovt', 'realdpi', 'm1']
drop_cols = ['year', 'realgdp', 'pop', 'cpi', 'growth_rate']
data.loc[:, pct_cols] = data.loc[:, pct_cols].pct_change(4)
In [261]:
data = pd.get_dummies(data.drop(drop_cols, axis=1), columns=['quarter'], drop_first=True).dropna()
In [262]:
data.head()
Out[262]:
| realcons | realinv | realgovt | realdpi | m1 | tbilrate | unemp | infl | realint | target | quarter_2 | quarter_3 | quarter_4 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0.04 | 0.16 | -0.02 | 0.04 | -0.00 | 3.50 | 5.20 | 2.31 | 1.19 | 0.00 | 0 | 0 | 0 |
| 5 | 0.03 | -0.04 | -0.04 | 0.02 | -0.01 | 2.68 | 5.20 | 0.14 | 2.55 | 0.00 | 1 | 0 | 0 |
| 6 | 0.02 | 0.02 | -0.03 | 0.03 | 0.00 | 2.36 | 5.60 | 2.70 | -0.34 | 0.00 | 0 | 1 | 0 |
| 7 | 0.02 | -0.13 | -0.02 | 0.02 | 0.01 | 2.29 | 6.30 | 1.21 | 1.08 | 0.00 | 0 | 0 | 1 |
| 8 | 0.01 | -0.20 | 0.03 | 0.01 | 0.02 | 2.37 | 6.80 | -0.40 | 2.77 | 0.00 | 0 | 0 | 0 |
In [263]:
data.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 198 entries, 4 to 201 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 realcons 198 non-null float64 1 realinv 198 non-null float64 2 realgovt 198 non-null float64 3 realdpi 198 non-null float64 4 m1 198 non-null float64 5 tbilrate 198 non-null float64 6 unemp 198 non-null float64 7 infl 198 non-null float64 8 realint 198 non-null float64 9 target 198 non-null float64 10 quarter_2 198 non-null uint8 11 quarter_3 198 non-null uint8 12 quarter_4 198 non-null uint8 dtypes: float64(10), uint8(3) memory usage: 17.6 KB
We use an intercept and convert the quarter values to dummy variables and train the logistic regression model as follows:
This produces the following summary for our model with 198 observations and 13 variables, including intercept: The summary indicates that the model has been trained using maximum likelihood and provides the maximized value of the log-likelihood function at -67.9.
In [264]:
model = sm.Logit(data.target, sm.add_constant(data.drop('target', axis=1)))
result = model.fit()
result.summary()
Optimization terminated successfully.
Current function value: 0.342965
Iterations 8
Out[264]:
| Dep. Variable: | target | No. Observations: | 198 |
|---|---|---|---|
| Model: | Logit | Df Residuals: | 185 |
| Method: | MLE | Df Model: | 12 |
| Date: | Sat, 24 Jun 2023 | Pseudo R-squ.: | 0.5022 |
| Time: | 16:58:12 | Log-Likelihood: | -67.907 |
| converged: | True | LL-Null: | -136.42 |
| Covariance Type: | nonrobust | LLR p-value: | 2.375e-23 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | -8.5881 | 1.908 | -4.502 | 0.000 | -12.327 | -4.849 |
| realcons | 130.1446 | 26.633 | 4.887 | 0.000 | 77.945 | 182.344 |
| realinv | 18.8414 | 4.053 | 4.648 | 0.000 | 10.897 | 26.786 |
| realgovt | -19.0318 | 6.010 | -3.166 | 0.002 | -30.812 | -7.252 |
| realdpi | -52.2473 | 19.912 | -2.624 | 0.009 | -91.275 | -13.220 |
| m1 | -1.3462 | 6.177 | -0.218 | 0.827 | -13.453 | 10.761 |
| tbilrate | 60.8607 | 44.350 | 1.372 | 0.170 | -26.063 | 147.784 |
| unemp | 0.9487 | 0.249 | 3.818 | 0.000 | 0.462 | 1.436 |
| infl | -60.9647 | 44.362 | -1.374 | 0.169 | -147.913 | 25.984 |
| realint | -61.0453 | 44.359 | -1.376 | 0.169 | -147.987 | 25.896 |
| quarter_2 | 0.1128 | 0.618 | 0.182 | 0.855 | -1.099 | 1.325 |
| quarter_3 | -0.1991 | 0.609 | -0.327 | 0.744 | -1.393 | 0.995 |
| quarter_4 | 0.0007 | 0.608 | 0.001 | 0.999 | -1.191 | 1.192 |
LL-Null 값인 -136.42는 절편만 포함된 상태에서 최대화된 로그-우도 함수의 결과입니다. 이 값은 의사 R2 통계량과 로그-우도비(LLR) 검정의 기초가 됩니다.
의사 R2 통계량은 최소 제곱법에서 제공되는 익숙한 R2의 대체물입니다. 이는 다음과 같이 널 모델 m0와 전체 모델 m1의 최대화된 로그-우도 함수의 비율을 기반으로 계산됩니다:
값은 0부터 시작하여 모델이 우도를 개선하지 않을 때부터 1까지 변동하며, 모델이 완벽하게 적합되고 로그-우도가 0에서 최대화될 때 1에 도달합니다. 따라서 높은 값은 더 좋은 적합을 나타냅니다.
In [265]:
plt.rc('figure', figsize=(12, 7))
plt.text(0.01, 0.05, str(result.summary()), {'fontsize': 14}, fontproperties = 'monospace')
plt.axis('off')
plt.tight_layout()
plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)
plt.savefig('logistic_example.png', bbox_inches='tight', dpi=300);
- 08_predicting_price_movements_with_logistic_regression.ipynb
Predicting stock price moves with Logistic Regression¶
Imports & Settings¶
In [266]:
import warnings
warnings.filterwarnings('ignore')
In [267]:
from pathlib import Path
import sys, os
from time import time
import pandas as pd
import numpy as np
from scipy.stats import spearmanr
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
In [274]:
!pip install --upgrade utils
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: utils in /usr/local/lib/python3.10/dist-packages (1.0.1)
In [ ]:
# sys.path.insert(1, os.path.join(sys.path[0], '..'))
# from utils import MultipleTimeSeriesCV
In [276]:
sns.set_style('darkgrid')
idx = pd.IndexSlice
In [277]:
YEAR = 252
Load Data¶
In [278]:
with pd.HDFStore('/content/data.h5') as store:
data = (store['model_data']
.dropna()
.drop(['open', 'close', 'low', 'high'], axis=1))
data = data.drop([c for c in data.columns if 'year' in c or 'lag' in c], axis=1)
Select Investment Universe¶
In [279]:
data = data[data.dollar_vol_rank<100]
Create Model Data¶
In [280]:
y = data.filter(like='target')
X = data.drop(y.columns, axis=1)
X = X.drop(['dollar_vol', 'dollar_vol_rank', 'volume', 'consumer_durables'], axis=1)
Logistic Regression¶
Define cross-validation parameters¶
In [281]:
train_period_length = 63
test_period_length = 10
lookahead =1
n_splits = int(3 * YEAR/test_period_length)
cv = MultipleTimeSeriesCV(n_splits=n_splits,
test_period_length=test_period_length,
lookahead=lookahead,
train_period_length=train_period_length)
In [282]:
target = f'target_{lookahead}d'
In [283]:
y.loc[:, 'label'] = (y[target] > 0).astype(int)
y.label.value_counts()
Out[283]:
1 56486 0 53189 Name: label, dtype: int64
In [284]:
Cs = np.logspace(-5, 5, 11)
In [285]:
cols = ['C', 'date', 'auc', 'ic', 'pval']
Run cross-validation¶
In [286]:
%%time
log_coeffs, log_scores, log_predictions = {}, [], []
for C in Cs:
print(C)
model = LogisticRegression(C=C,
fit_intercept=True,
random_state=42,
n_jobs=-1)
pipe = Pipeline([
('scaler', StandardScaler()),
('model', model)])
ics = aucs = 0
start = time()
coeffs = []
for i, (train_idx, test_idx) in enumerate(cv.split(X), 1):
X_train, y_train, = X.iloc[train_idx], y.label.iloc[train_idx]
pipe.fit(X=X_train, y=y_train)
X_test, y_test = X.iloc[test_idx], y.label.iloc[test_idx]
actuals = y[target].iloc[test_idx]
if len(y_test) < 10 or len(np.unique(y_test)) < 2:
continue
y_score = pipe.predict_proba(X_test)[:, 1]
auc = roc_auc_score(y_score=y_score, y_true=y_test)
actuals = y[target].iloc[test_idx]
ic, pval = spearmanr(y_score, actuals)
log_predictions.append(y_test.to_frame('labels').assign(
predicted=y_score, C=C, actuals=actuals))
date = y_test.index.get_level_values('date').min()
log_scores.append([C, date, auc, ic * 100, pval])
coeffs.append(pipe.named_steps['model'].coef_)
ics += ic
aucs += auc
if i % 10 == 0:
print(f'\t{time()-start:5.1f} | {i:03} | {ics/i:>7.2%} | {aucs/i:>7.2%}')
log_coeffs[C] = np.mean(coeffs, axis=0).squeeze()
1e-05 13.8 | 010 | -0.31% | 50.42% 24.9 | 020 | 1.89% | 51.83% 35.8 | 030 | 2.84% | 52.01% 46.4 | 040 | 3.29% | 51.98% 57.1 | 050 | 3.97% | 52.44% 67.8 | 060 | 3.96% | 52.27% 78.5 | 070 | 4.73% | 52.59% 0.0001 10.9 | 010 | -0.06% | 50.62% 22.1 | 020 | 2.23% | 52.01% 32.8 | 030 | 3.20% | 52.26% 43.4 | 040 | 3.34% | 52.08% 54.1 | 050 | 4.02% | 52.53% 64.7 | 060 | 4.02% | 52.33% 75.4 | 070 | 4.83% | 52.67% 0.001 10.6 | 010 | 0.42% | 50.96% 21.4 | 020 | 2.53% | 52.14% 32.1 | 030 | 3.58% | 52.48% 42.8 | 040 | 3.17% | 52.07% 53.6 | 050 | 3.83% | 52.49% 64.3 | 060 | 4.03% | 52.33% 75.1 | 070 | 4.88% | 52.70% 0.01 10.9 | 010 | 0.68% | 51.13% 21.7 | 020 | 2.39% | 51.97% 32.5 | 030 | 3.64% | 52.41% 43.3 | 040 | 3.12% | 51.94% 54.1 | 050 | 3.92% | 52.46% 64.9 | 060 | 4.16% | 52.30% 75.7 | 070 | 4.91% | 52.64% 0.1 11.1 | 010 | 0.65% | 51.11% 22.0 | 020 | 2.17% | 51.80% 32.8 | 030 | 3.49% | 52.28% 44.5 | 040 | 2.94% | 51.80% 55.5 | 050 | 3.74% | 52.31% 66.5 | 060 | 3.93% | 52.15% 77.3 | 070 | 4.59% | 52.46% 1.0 11.0 | 010 | 0.60% | 51.07% 21.9 | 020 | 2.13% | 51.77% 32.7 | 030 | 3.45% | 52.26% 43.7 | 040 | 2.90% | 51.77% 54.6 | 050 | 3.68% | 52.28% 65.5 | 060 | 3.86% | 52.11% 76.5 | 070 | 4.50% | 52.41% 10.0 10.9 | 010 | 0.59% | 51.07% 21.8 | 020 | 2.12% | 51.76% 32.8 | 030 | 3.45% | 52.25% 43.6 | 040 | 2.89% | 51.77% 54.6 | 050 | 3.67% | 52.27% 65.6 | 060 | 3.85% | 52.11% 76.6 | 070 | 4.49% | 52.41% 100.0 11.0 | 010 | 0.59% | 51.07% 22.0 | 020 | 2.11% | 51.76% 33.0 | 030 | 3.45% | 52.25% 44.5 | 040 | 2.89% | 51.77% 56.0 | 050 | 3.67% | 52.27% 67.0 | 060 | 3.85% | 52.11% 77.9 | 070 | 4.49% | 52.41% 1000.0 10.9 | 010 | 0.59% | 51.07% 21.9 | 020 | 2.11% | 51.76% 32.8 | 030 | 3.44% | 52.25% 44.1 | 040 | 2.89% | 51.77% 55.1 | 050 | 3.67% | 52.27% 66.0 | 060 | 3.85% | 52.11% 76.8 | 070 | 4.49% | 52.41% 10000.0 10.9 | 010 | 0.59% | 51.07% 21.7 | 020 | 2.11% | 51.76% 32.7 | 030 | 3.45% | 52.25% 43.9 | 040 | 2.89% | 51.77% 54.7 | 050 | 3.67% | 52.27% 65.7 | 060 | 3.85% | 52.11% 76.5 | 070 | 4.49% | 52.41% 100000.0 11.0 | 010 | 0.59% | 51.07% 21.9 | 020 | 2.11% | 51.76% 32.7 | 030 | 3.45% | 52.25% 43.7 | 040 | 2.89% | 51.77% 54.7 | 050 | 3.67% | 52.27% 65.7 | 060 | 3.85% | 52.11% 76.7 | 070 | 4.49% | 52.41% CPU times: user 49.1 s, sys: 1min 15s, total: 2min 5s Wall time: 15min 2s
Evaluate Results¶
In [287]:
log_scores = pd.DataFrame(log_scores, columns=cols)
log_scores.to_hdf('/content/data.h5', 'logistic/scores')
log_coeffs = pd.DataFrame(log_coeffs, index=X.columns).T
log_coeffs.to_hdf('/content/data.h5', 'logistic/coeffs')
log_predictions = pd.concat(log_predictions)
log_predictions.to_hdf('/content/data.h5', 'logistic/predictions')
In [288]:
log_scores = pd.read_hdf('/content/data.h5', 'logistic/scores')
In [289]:
log_scores.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 825 entries, 0 to 824 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 C 825 non-null float64 1 date 825 non-null datetime64[ns] 2 auc 825 non-null float64 3 ic 825 non-null float64 4 pval 825 non-null float64 dtypes: datetime64[ns](1), float64(4) memory usage: 38.7 KB
In [290]:
log_scores.groupby('C').auc.describe()
Out[290]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| C | ||||||||
| 0.00 | 75.00 | 0.52 | 0.04 | 0.43 | 0.50 | 0.52 | 0.55 | 0.63 |
| 0.00 | 75.00 | 0.53 | 0.04 | 0.44 | 0.50 | 0.52 | 0.55 | 0.64 |
| 0.00 | 75.00 | 0.53 | 0.04 | 0.44 | 0.50 | 0.52 | 0.55 | 0.64 |
| 0.01 | 75.00 | 0.53 | 0.04 | 0.44 | 0.50 | 0.52 | 0.55 | 0.64 |
| 0.10 | 75.00 | 0.52 | 0.04 | 0.43 | 0.50 | 0.52 | 0.55 | 0.62 |
| 1.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
| 10.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
| 100.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
| 1,000.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
| 10,000.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
| 100,000.00 | 75.00 | 0.52 | 0.04 | 0.42 | 0.50 | 0.52 | 0.55 | 0.62 |
Plot Validation Scores¶
In [291]:
def plot_ic_distribution(df, ax=None):
if ax is not None:
sns.distplot(df.ic, ax=ax)
else:
ax = sns.distplot(df.ic)
mean, median = df.ic.mean(), df.ic.median()
ax.axvline(0, lw=1, ls='--', c='k')
ax.text(x=.05, y=.9, s=f'Mean: {mean:8.2f}\nMedian: {median:5.2f}',
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
ax.set_xlabel('Information Coefficient')
sns.despine()
plt.tight_layout()
In [292]:
fig, axes= plt.subplots(ncols=2, figsize=(15, 5))
sns.lineplot(x='C', y='auc', data=log_scores, estimator=np.mean, label='Mean', ax=axes[0])
by_alpha = log_scores.groupby('C').auc.agg(['mean', 'median'])
best_auc = by_alpha['mean'].idxmax()
by_alpha['median'].plot(logx=True, ax=axes[0], label='Median', xlim=(10e-6, 10e5))
axes[0].axvline(best_auc, ls='--', c='k', lw=1, label='Max. Mean')
axes[0].axvline(by_alpha['median'].idxmax(), ls='-.', c='k', lw=1, label='Max. Median')
axes[0].legend()
axes[0].set_ylabel('AUC')
axes[0].set_xscale('log')
axes[0].set_title('Area Under the Curve')
plot_ic_distribution(log_scores[log_scores.C==best_auc], ax=axes[1])
axes[1].set_title('Information Coefficient')
fig.suptitle('Logistic Regression', fontsize=14)
sns.despine()
fig.tight_layout()
fig.subplots_adjust(top=.9);
In [ ]: