CH04 금융 특성 공학: 알파 팩터 리서치¶
알고리즘 트레이딩 전략은 벤치마크 대비 양의 수익률을 내고자 언제 자산을 매수 or 매도해야 하는지 가리키는 신호에 의해 결정된다.
- 알파: 벤치마크에 대한 노출에 의해 설명되지 않는 자산의 수익률 부분
- 알파 팩터: 알파의 신호
4장에서 다루는 내용
- 존재하는 팩터의 범주, 작동 이유, 측정 방법
- 넘파이, 판다스, TA-Lib를 사용한 알파 팩터 생성
- 웨이블릿과 칼만 필터를 사용해 데이터 잡음을 제거하는 방법
- 오프라인 및 퀀토피안 플랫폼에서 집라인을 사용해 개별 및 다중 알파 팩터 테스트
- 여러 척도 중 정보 계수(IC)를 사용한 예측 성과 및 회전률을 평가하는 방법
알파 팩터 실무: 데이터부터 신호까지¶
알파 팩터: 자산 가격의 움직임을 예측하는 것을 목표로 하는 원시 데이터의 변환
- 자산 수익률을 높이는 리스크를 포착하게 설계됨
- 팩터는 하나 또는 여러 개의 입력 변수를 결합하지만 전략이 팩터를 평가할 때는 언제나 각 자산에 대해 단일 값을 취함
- 트레이딩 결정은 여러 자산에 걸쳐 상대적 팩터 값이나 단일 자산에 대해 패턴에 의존함
알파 팩터 리서치와 실행 워크플로
- 리서치 (4장)
- 예측 데이터 수집
- 투자 유니버스 정의
- 알파 팩터 설계
- 알파 팩터 결합
- 실행 (5장)
- 포트폴리오 최적화
- 트레이드 실행
- 리서치 (4장)
알파 팩터 공학¶
- 알파 팩터: 예측 신호를 포함한 시장 데이터, 기본적 데이터와 대체 데이터를 변환한 것
- 성장률, 물가 상승률, 변동성, 생산성과 인구 통계적 리스크와 같은 기본적인 경제 전반의 거시 경제 데이터를 묘사한다.
- 거래 가능하며 시장 가격에 의해 반영되는 가치-성장 투자 및 모멘텀 투자와 같은 투자 스타일을 표현한다.
- 금융시장의 경제적 또는 제도적 설정 또는 투자자들의 편향을 포함한 투자자 행동을 기반으로 가격 움직임을 설명한다.
- 팩터가 일관성 있는 결과를 내도록 보장하려면 모멘텀, 가치, 변동성, 기업의 질과 같은 다양한 기존 팩터 범주와 관련 논리를 기반으로 의미 있는 경제적 직관이 있어야 한다.
- 합리성 기반 경제학: 팩터들이 나쁜 시기의 낮은 수익률을 보상하고자 장기적으로 높은 수익률을 가진다고 설명한다.
- 행동 경제학: 팩터 리스크 프리미엄은 경제 주체의 편향을 가진 행동, 즉 완전히 합리적이지 않은 행동이 없어지지 않음으로부터 비롯된다고 설명한다.
알파 팩터는 변수의 시간에 따른 절대적 또는 상대적 변화, 데이터 시리즈 간의 비율, 단순 또는 지수 이동 평균과 같은 일정 기간 동안의 합계와 같은 단순한 산술적 연산을 사용해 원시 시장, 기본 또는 대체 데이터를 변환함으로써 생성된다.
- 공급에 대한 수요의 상대 강도 지표(relative strength index)와 같은 가격과 거래량 패턴의 기술적 분석 및 증권의 기본적 분석에 등장하는 척도들도 포함
수십 년간의 팩터 리서치를 바탕으로 구축¶
- 이상적인 리스크 팩터
- 상호 독립적
- 양의 리스크 프리미엄 창출
- 리스크의 모든 차원을 생성해 완전한 집합 형성
- 주어진 자산군의 자산의 체계적 리스크 설명 가능
여기서 금융 연구와 트레이딩 응용에서 유명한 몇 가지 주요 팩터 범주를 검토하고, 경제적 논리를 설명하고, 이들 수익률의 동인들을 포착하고자 전형적으로 사용하는 척도를 제시한다.
모멘텀과 심리: 추세는 당신의 친구다¶
- 모멘텀 투자
- 모멘텀 팩터는 일정 기간에 걸쳐 성과가 좋은 자산들을 매수하고, 성과가 안 좋은 자산들을 공매도하도록 설계된다.
모멘텀 전략의 전제
- 자산 가격이 양의 시계열 상관관계로 반영되는 추세
- 가격 모멘텀은 과거 가격 수익률들만으로는 미래의 성과를 예측할 수 없다는 효율적 시장 가설을 부정
- 가격 모멘텀은 여러 자산군에 걸쳐 양의 수익률을 보임
모멘텀 팩터는 HML(High-Minus-Low) 가치 팩터, RMW(Robust-Minus-Week) 수익성 팩터 CMA(Conservertive-Minus-Aggressive) 투자 팩터를 포함한다.
- 주식 프리미엄은 시장 수익률(ex. S&P 500)과 무위험 이자율과의 차이이다.
모멘텀과 심리가 초과 수익률을 낳는 이유¶
- 모멘텀 효과의 이유
- 투자자의 행동
- 행동 경제학적 이유: 투자자들이 새로운 정보를 상이한 속도로 소화하면서 나타나는 시장 뉴스에 대한 과소반응과 과대반응의 바이어스를 반영
- 리스크 자산과 경제와의 양의 피드백 루프
- 경제 성장은 주식시장을 상승시키고, 이에 따른 부의 효과는 더 높은 지출을 통해 경제로 재유입되고 이는 다시 성장을 촉진한다.
- 음의 피드백이 리버설을 초래하는 채권, 외환, 상품이 훨씬 짧은 투자 기간을 요구하는 반면, 이러한 가격과 경제의 양의 피드백은 종종 주식과 신용 상품의 모멘텀을 장기로 연장시킨다.
- 시장 마찰로 인한 지속적인 수요와 공급의 불균형
- 상품 생산이 수요 추세에 적응하기 위해 상당한 시간이 걸린다.
- 지속적인 공급 부족이 가격 상승 모멘텀을 유발하고 지지할 수 있다.
- 시장 미시 구조
- 투자자들은 손절매, 고정비율 포트폴리오 보험(CPPI), 동적 델타 헤징, 방어적 풋과 같은 옵션 기반 트레이딩 전략을 사용한다.
- .이는 자산의 성과가 나쁠 때 매도하고 자산의 성과가 좋을 때 매수하도록 한 사전 약정을 의미하여 모멘텀을 초래한다.
- 유사하게 리스크 패리티 전략도 좋은 성과를 보이는 변동성이 낮은 자산을 매수하고 음의 성과를 보이는 변동성이 높은 자산을 매도하는 경향이 있다.
- 이를 사용하는 포트폴리오의 자동 리밸런싱은 가격 모멘텀을 강화한다.
모멘텀과 심리를 측정하는 방법¶
모멘텀 팩터는 추세와 패턴을 식별하여 전통적인 가격 시계열 변화에서 도출된다. 이는 전통 자산군 내 또는 저통 자산군 간에, 그리고 여러 기간에서 자산을 횡단면적으로 비교하거나 자산의 시계열을 분석함으로써 절대적 수익률이나 상대적 수익률을 기반으로 구축될 수 있다.
- 모멘텀 지표
| 팩터 | 설명 | 공식 |
|---|---|---|
| 상대 강도 지수 (RSI) | 주가 상승과 하락의 강도를 비교하여 추세의 강도를 측정하는 지표, RSI는 최근 가격 변화를 종목 간 비교해 과매수 또는 과매도한 종목을 확인한다. 높은 RSI(70 이상)는 과매수, 낮은 RSI(30이하)는 과매도를 나타낸다. |
RSI = 100 - (100 / (1 + RS)), RS = (avg_gain / avg_loss) |
| 가격 모멘텀 | 주가의 현재 가격을 이전 기간의 가격과 비교하여 추세를 판단하는 지표, 주어진 과거 거래일수에 대한 총 수익률을 계산한다. |
Momentum = (현재 가격 / 이전 기간 가격) - 1 |
| 변동성 조정 12개월 가격 모멘텀 | 12개월 동안의 가격 모멘텀을 변동성으로 조정한 지표 수익률을 표준편차로 나눔으로써 과거 12개월의 총수익률을 정규화한다. |
Volatility Adjusted Momentum = (현재 가격 / 이전 기간 가격 - 1) / Volatility |
| 가격 가속도 | 주가의 가속도를 측정하여 추세의 세기를 나타내는 지표, 장단와 단기에 대한 일일 가격에 대한 선형 회귀식을 이용해 (변동성이 조정된) 추세의 기울기를 계산하고, 가격 가속도의 척도로 기울기의 변화를 계산한다. |
가격 가속도 = (현재 가격 / 이전 기간 가격) ** (1 / 기간) - 1 |
| 52주 최고가 대비 변화률 | 주가의 52주 최고가에 대한 현재 가격의 변화를 나타내는 지표 | 52주 최고가 대비 변화률 = (현재 가격 - 52주 최고가) / 52주 최고가 * 100 |
- 심리 지표
| 지표 | 설명 |
|---|---|
| 이익 추정수 | 애널리스트 커버와 정보 불확실성의 대체적 척도로서 컨센서스 추정치의 수를 사용해 주식의 순위를 매긴다. |
| N개월간 종목 추천의 변화 | 과거 N개월간의 컨센서스 추천의 변화로 주식의 순위를 매기는데, 강한 매도에서 매도 또는 매수에서 강한 매수 등 개선된 경우를 바람직한 경우로 본다. |
| 12개월 발생 주식수의 변화 | 최근 12개월 동안의 분할 조정된 발행 주식수의 변화를 측정한다. 음의 변화는 자사주 매입(stock bayback)를 의미한다. |
| 6개월간 목표 주가의 변화 | 평균 애널리스트의 목표 가격에 대한 6개월간 변화를 추적한다. 양의 변화가 클수록 바람직하다. |
| 순이익의 조정 | 이익 추정치에 대한 상향 조정과 하향 조정의 차이를 총조정수의 %로 표현한다. |
| 발행 주식 대비 대차잔고 비율 | 현재 공매도 돼 있는 주식을 차입해 매도하고 이후 다시 매입할 주식의 발행 주식 대비 %를 측정한다. |
가치 팩터¶
가치 팩터: 상대적으로 저렴한 과소평가된 자산을 매수하는 신호를 제공하고, 과대평가된 자산에 대해 매도 신호를 제공하도록 설계된다.
- 기본적 가치보다 상대적으로 낮은 가격을 가진 주식이 시가 가중 벤치마크를 초과하는 수익률을 가져다 주는 상관관계
가치 전략 모델
- 공정 가치: 절대 가격 수준, 다른 자산과의 상대적인 스프레드 또는 자산이 거래될 가격의 범위로 정의한다.
- 기본 가치
상대적 가치 전략¶
가치 전략은 자산의 공정 가치에 대한 가격의 평균 회귀에 의존한다. 이 전략은 가격은 과대반응 또는 허딩과 같은 행동 경제적 효과 또는 일시적 시장 충격, 장기 수급 마찰과 같은 유동성 효과에 의해 공정 가치로부터 일시적으로 벗어날 수 있다고 가정한다. 가치주는 종종 평균 회귀에 의존하므로 모멘텀 팩터의 속성과는 반대의 속정을 보인다. 주식에 있어 가치주와 반대되는 개념은 성장 예상으로 인해 고평가된 성장주이다.
가치 팩터는 기본적 및 시장 가치 평가, 통계적 차익 거래 및 자산 간 상대 가치 전략을 포함하는 광범위한 체계적 전략을 가능하게 한다.
- 기본적 가치 전략
- 목표 자산국에 따라 관련된 경제적 및 기본적 지표들로부터 공정 자산 가치를 도출한다.
- 시장 가치 전략
- 통계적 모델이나 머신러닝 모델을 사용해 유동성 공급의 비효율성으로 인해 왜곡된 가격을 찾는다.
- 자산 간 상대 가치 전략
- 자산 클래스 간 상대적 가격 왜곡에 주목한다.
가치 팩터가 수익률을 예측하는데 데 도움이 되는 이유¶
- 합리성 기반 설명: 가치 프리미엄은 더 높은 실제 또는 인지되는 리스크를 보상한다.
- 행동 경제 기반 설명: 가치 프리미엄은 손실 회피와 멘탈 회계 편향으로 설명된다.
이러한 수익률 차이 기대는 가치 프리미엄을 산출한다.
가치 효과를 포착하는 방법¶
가치 대리 지표
- 현금 흐름 수익률
- 잉여 현금 흐름 수익률
- 투하 자본에 대한 현금 흐름 수익률(CFROIC)
- 총자산 대비 현금 흐름
- 기업 가치 대비 현금 흐름
- 기업 가치 대비 EBITDA
- 이익 수익률(최근 1년)
- 이익 수익률(미래 1년)
- PEG 비율
- 섹터 대비 P/E(1년 미래)
- 매출 수익률
- 매출 수익률(FY1)
- 장부 가치 수익률
- 배당 수익률
변동성과 규모 이례 현상¶
- 규모 효과: 오래된 리스크 팩터 중 하나로, 낮은 시가 총액 주식의 초과성과 관련 있다.
- 저변동성 팩터: 평균 이하의 변동성, 베타 또는 특이 리스크로 주식의 초과 수익률을 포착한다.
시장 가치가 큰 주식은 변동성이 낮은 경향이 있어 흔히 전통적인 규모 팩터는 더 최근의 변동성 팩터와 결합된다.
- 자본 자산 가격 모델(CAPM)과 다른 가격 결정 모델에 의하면 높은 리스크는 높은 수익률을 얻어야만 한다.
변동성과 규모가 수익률을 예측하는 이유¶
저변동성 이례 현상은 효율적 시장 가설과 CAPM 가정과 배치되며, 여러 행동 경제학적 설명이 제시된다.
- 복권 효과
- 높은 변동성 주식 -> 높은 가격
- 낮은 변동성 주식 -> 낮은 가격
- 대표 편향
- 투자자들이 일부 매우 유명한 변동성 높은 주식의 성공을 모든 변동성 높은 주식에 외삽한다.
- 자만
주요 척도¶
저변동성 식별 지표
- 실현 변동성(표준 편차)
- 예측(내재) 변동성과 상관관계를 포함
수익률을 예측하는 알파 팩터 공학¶
판다스와 넘파이를 사용한 팩터 공학¶
01_feature_engineering.ipynb¶
How to transform data into factors¶
Based on a conceptual understanding of key factor categories, their rationale and popular metrics, a key task is to identify new factors that may better capture the risks embodied by the return drivers laid out previously, or to find new ones.
In either case, it will be important to compare the performance of innovative factors to that of known factors to identify incremental signal gains.
We create the dataset here and store it in our data folder to facilitate reuse in later chapters.
Imports & Settings¶
In [ ]:
import warnings
warnings.filterwarnings('ignore')
In [ ]:
%matplotlib inline
from datetime import datetime
import pandas as pd
import pandas_datareader.data as web
# replaces pyfinance.ols.PandasRollingOLS (no longer maintained)
from statsmodels.regression.rolling import RollingOLS
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
In [ ]:
sns.set_style('whitegrid')
idx = pd.IndexSlice
Get Data¶
The assets.h5 store can be generated using the the notebook create_datasets in the data directory in the root directory of this repo for instruction to download the following dataset.
We load the Quandl stock price datasets covering the US equity markets 2000-18 using pd.IndexSlice to perform a slice operation on the pd.MultiIndex, select the adjusted close price and unpivot the column to convert the DataFrame to wide format with tickers in the columns and timestamps in the rows:
Set data store location:
In [ ]:
from google.colab import drive
drive.mount('/content/drive') # 구글 드라이브를 사용하는 경우
DATA_STORE = 'assets.h5'
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [ ]:
!cp /content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/assets.h5 ./assets.h5
In [ ]:
START = 2000
END = 2018
In [ ]:
with pd.HDFStore(DATA_STORE) as store:
prices = (store['quandl/wiki/prices']
.loc[idx[str(START):str(END), :], 'adj_close']
.unstack('ticker'))
stocks = store['us_equities/stocks'].loc[:, ['marketcap', 'ipoyear', 'sector']]
In [ ]:
prices.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 4706 entries, 2000-01-03 to 2018-03-27 Columns: 3199 entries, A to ZUMZ dtypes: float64(3199) memory usage: 114.9 MB
In [ ]:
stocks.info()
<class 'pandas.core.frame.DataFrame'> Index: 6834 entries, PIH to ZYME Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 marketcap 5766 non-null float64 1 ipoyear 3038 non-null float64 2 sector 5288 non-null object dtypes: float64(2), object(1) memory usage: 213.6+ KB
Keep data with stock info¶
- 데이터 로딩 및 슬라이싱 재구성
Remove stocks duplicates and align index names for later joining.
In [ ]:
stocks = stocks[~stocks.index.duplicated()]
stocks.index.name = 'ticker'
Get tickers with both price information and metdata
In [ ]:
shared = prices.columns.intersection(stocks.index)
In [ ]:
stocks = stocks.loc[shared, :]
stocks.info()
<class 'pandas.core.frame.DataFrame'> Index: 2412 entries, A to ZUMZ Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 marketcap 2407 non-null float64 1 ipoyear 1065 non-null float64 2 sector 2372 non-null object dtypes: float64(2), object(1) memory usage: 75.4+ KB
In [ ]:
prices = prices.loc[:, shared]
prices.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 4706 entries, 2000-01-03 to 2018-03-27 Columns: 2412 entries, A to ZUMZ dtypes: float64(2412) memory usage: 86.6 MB
In [ ]:
assert prices.shape[1] == stocks.shape[0]
Create monthly return series¶
- 표본 빈도 바꾸기: 일별에서 월별 빈도로
To reduce training time and experiment with strategies for longer time horizons, we convert the business-daily data to month-end frequency using the available adjusted close price:
In [ ]:
monthly_prices = prices.resample('M').last()
- 과거 여러 기간을 위한 수익률 계산
시계열 동역학을 반영하는 예를 들어 모멘텀 패턴을 포착하기 위해, 우리는 .pct_change(n_periods) 메서드를 사용하여 히스토리컬 수익률을 계산합니다. 이는 랙(lag)에 따라 식별된 다양한 월간 기간에 대한 수익률을 의미합니다.
그런 다음 .stack() 메서드를 사용하여 넓은 형식(wide format)의 결과를 다시 긴 형식(long format)으로 변환하고, .pipe()를 사용하여 결과 DataFrame에 .clip() 메서드를 적용하여 수익률을 [1%, 99%] 수준에서 winsorize합니다. 즉, 이상치를 해당 백분위로 제한합니다.
마지막으로, 기하 평균을 사용하여 수익률을 정규화합니다. MultiIndex 레벨의 순서를 변경하기 위해 .swaplevel()을 사용한 후, 1에서 12개월까지 범위가 있는 6개 기간에 대한 복리 월간 수익률을 얻을 수 있습니다.
In [ ]:
monthly_prices.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 219 entries, 2000-01-31 to 2018-03-31 Freq: M Columns: 2412 entries, A to ZUMZ dtypes: float64(2412) memory usage: 4.0 MB
In [ ]:
outlier_cutoff = 0.01
data = pd.DataFrame()
lags = [1, 2, 3, 6, 9, 12]
for lag in lags:
data[f'return_{lag}m'] = (monthly_prices
.pct_change(lag)
.stack()
.pipe(lambda x: x.clip(lower=x.quantile(outlier_cutoff),
upper=x.quantile(1-outlier_cutoff)))
.add(1)
.pow(1/lag)
.sub(1)
)
data = data.swaplevel().dropna()
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 399525 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 399525 non-null float64
1 return_2m 399525 non-null float64
2 return_3m 399525 non-null float64
3 return_6m 399525 non-null float64
4 return_9m 399525 non-null float64
5 return_12m 399525 non-null float64
dtypes: float64(6)
memory usage: 19.9+ MB
Drop stocks with less than 10 yrs of returns¶
In [ ]:
min_obs = 120
nobs = data.groupby(level='ticker').size()
keep = nobs[nobs>min_obs].index
data = data.loc[idx[keep,:], :]
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
dtypes: float64(6)
memory usage: 18.0+ MB
In [ ]:
data.describe()
Out[ ]:
| return_1m | return_2m | return_3m | return_6m | return_9m | return_12m | |
|---|---|---|---|---|---|---|
| count | 360752.000000 | 360752.000000 | 360752.000000 | 360752.000000 | 360752.000000 | 360752.000000 |
| mean | 0.012255 | 0.009213 | 0.008181 | 0.007025 | 0.006552 | 0.006296 |
| std | 0.114236 | 0.081170 | 0.066584 | 0.048474 | 0.039897 | 0.034792 |
| min | -0.329564 | -0.255452 | -0.214783 | -0.162063 | -0.131996 | -0.114283 |
| 25% | -0.046464 | -0.030716 | -0.023961 | -0.014922 | -0.011182 | -0.009064 |
| 50% | 0.009448 | 0.009748 | 0.009744 | 0.009378 | 0.008982 | 0.008726 |
| 75% | 0.066000 | 0.049249 | 0.042069 | 0.031971 | 0.027183 | 0.024615 |
| max | 0.430943 | 0.281819 | 0.221789 | 0.154555 | 0.124718 | 0.106371 |
In [ ]:
# cmap = sns.diverging_palette(10, 220, as_cmap=True)
sns.clustermap(data.corr('spearman'), annot=True, center=0, cmap='Blues');
We are left with 1,670 tickers.
In [ ]:
data.index.get_level_values('ticker').nunique()
Out[ ]:
1838
Rolling Factor Betas¶
- 팩터 벡터의 계산
Chapter 9, Time Series Models에서 선형 회귀를 사용하여 자산의 일반적인 리스크 요인에 대한 노출을 추정하기 위해 Fama-French 데이터를 소개합니다.
시장 리스크, 크기, 가치, 영업이익률, 투자 등 5가지 Fama-French 요인은 경험적으로 자산 수익률을 설명하는 데 기여하며, 포트폴리오의 리스크/수익 프로필을 평가하는 데 자주 사용됩니다. 따라서 미래 수익률을 예측하는 모델에 과거 요인 노출을 금융 특성으로 포함하는 것이 자연스럽습니다.
pandas-datareader를 사용하여 과거 요인 수익률에 액세스하고, statsmodels 라이브러리의 RollingOLS 이동 회귀 기능을 사용하여 과거 노출을 다음과 같이 추정할 수 있습니다:
데이터셋의 주식에 대한 5가지 요인인 시장 리스크, 크기, 가치, 영업이익률 및 투자에 대한 요인 노출을 추정하기 위해 Fama-French 연구 요인을 사용합니다.
In [ ]:
factors = ['Mkt-RF', 'SMB', 'HML', 'RMW', 'CMA']
factor_data = web.DataReader('F-F_Research_Data_5_Factors_2x3', 'famafrench', start='2000')[0].drop('RF', axis=1)
factor_data.index = factor_data.index.to_timestamp()
factor_data = factor_data.resample('M').last().div(100)
factor_data.index.name = 'date'
factor_data.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 280 entries, 2000-01-31 to 2023-04-30 Freq: M Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Mkt-RF 280 non-null float64 1 SMB 280 non-null float64 2 HML 280 non-null float64 3 RMW 280 non-null float64 4 CMA 280 non-null float64 dtypes: float64(5) memory usage: 13.1 KB
In [ ]:
factor_data = factor_data.join(data['return_1m']).sort_index()
factor_data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Mkt-RF 360752 non-null float64
1 SMB 360752 non-null float64
2 HML 360752 non-null float64
3 RMW 360752 non-null float64
4 CMA 360752 non-null float64
5 return_1m 360752 non-null float64
dtypes: float64(6)
memory usage: 18.0+ MB
In [ ]:
T = 24
betas = (factor_data.groupby(level='ticker',
group_keys=False)
.apply(lambda x: RollingOLS(endog=x.return_1m,
exog=sm.add_constant(x.drop('return_1m', axis=1)),
window=min(T, x.shape[0]-1))
.fit(params_only=True)
.params
.drop('const', axis=1)))
In [ ]:
betas.describe().join(betas.sum(1).describe().to_frame('total'))
Out[ ]:
| Mkt-RF | SMB | HML | RMW | CMA | total | |
|---|---|---|---|---|---|---|
| count | 318478.000000 | 318478.000000 | 318478.000000 | 318478.000000 | 318478.000000 | 360752.000000 |
| mean | 0.978877 | 0.626574 | 0.123491 | -0.062149 | 0.016887 | 1.486381 |
| std | 0.917586 | 1.254663 | 1.602559 | 1.907059 | 2.155855 | 3.305914 |
| min | -9.825518 | -10.420754 | -15.215922 | -23.005713 | -18.423557 | -33.251810 |
| 25% | 0.463498 | -0.119207 | -0.706456 | -0.973597 | -1.071361 | 0.000000 |
| 50% | 0.928597 | 0.541546 | 0.096100 | 0.038055 | 0.040629 | 1.214857 |
| 75% | 1.444065 | 1.303979 | 0.947862 | 0.949469 | 1.135087 | 3.146895 |
| max | 10.858294 | 10.459489 | 15.094963 | 16.977443 | 16.657684 | 34.097389 |
In [ ]:
cmap = sns.diverging_palette(10, 220, as_cmap=True)
sns.clustermap(betas.corr(), annot=True, cmap=cmap, center=0);
In [ ]:
data = (data
.join(betas
.groupby(level='ticker')
.shift()))
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 316640 non-null float64
7 SMB 316640 non-null float64
8 HML 316640 non-null float64
9 RMW 316640 non-null float64
10 CMA 316640 non-null float64
dtypes: float64(11)
memory usage: 39.8+ MB
Impute mean for missing factor betas¶
In [ ]:
data.loc[:, factors] = data.groupby('ticker')[factors].apply(lambda x: x.fillna(x.mean()))
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 360752 non-null float64
7 SMB 360752 non-null float64
8 HML 360752 non-null float64
9 RMW 360752 non-null float64
10 CMA 360752 non-null float64
dtypes: float64(11)
memory usage: 39.8+ MB
Momentum factors¶
- 모멘텀 팩터를 추가하는 법
이러한 결과를 사용하여 다음과 같이 긴 기간 동안의 수익률과 가장 최근 월간 수익률 간의 차이, 그리고 3개월과 12개월 수익률 간의 차이를 기반으로 모멘텀 요인을 계산할 수 있습니다:
In [ ]:
for lag in [2,3,6,9,12]:
data[f'momentum_{lag}'] = data[f'return_{lag}m'].sub(data.return_1m)
data[f'momentum_3_12'] = data[f'return_12m'].sub(data.return_3m)
Date Indicators¶
- 시간 지표를 추가해 계절 효과를 포착하는 방법
In [ ]:
dates = data.index.get_level_values('date')
data['year'] = dates.year
data['month'] = dates.month
Lagged returns¶
- 래그 수익률 특성을 만드는 방법
과거 수익률을 현재 관측값과 연관된 입력 변수 또는 특성으로 사용하기 위해, 우리는 .shift() 메서드를 사용하여 히스토리컬 수익률을 현재 기간으로 이동시킵니다.
In [ ]:
for t in range(1, 7):
data[f'return_1m_t-{t}'] = data.groupby(level='ticker').return_1m.shift(t)
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 25 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 360752 non-null float64
7 SMB 360752 non-null float64
8 HML 360752 non-null float64
9 RMW 360752 non-null float64
10 CMA 360752 non-null float64
11 momentum_2 360752 non-null float64
12 momentum_3 360752 non-null float64
13 momentum_6 360752 non-null float64
14 momentum_9 360752 non-null float64
15 momentum_12 360752 non-null float64
16 momentum_3_12 360752 non-null float64
17 year 360752 non-null int64
18 month 360752 non-null int64
19 return_1m_t-1 358914 non-null float64
20 return_1m_t-2 357076 non-null float64
21 return_1m_t-3 355238 non-null float64
22 return_1m_t-4 353400 non-null float64
23 return_1m_t-5 351562 non-null float64
24 return_1m_t-6 349724 non-null float64
dtypes: float64(23), int64(2)
memory usage: 78.3+ MB
Target: Holding Period Returns¶
- 미래 수익률을 만드는 방법
Similarly, to compute returns for various holding periods, we use the normalized period returns computed previously and shift them back to align them with the current financial features
In [ ]:
for t in [1,2,3,6,12]:
data[f'target_{t}m'] = data.groupby(level='ticker')[f'return_{t}m'].shift(-t)
In [ ]:
cols = ['target_1m',
'target_2m',
'target_3m',
'return_1m',
'return_2m',
'return_3m',
'return_1m_t-1',
'return_1m_t-2',
'return_1m_t-3']
data[cols].dropna().sort_index().head(10)
Out[ ]:
| target_1m | target_2m | target_3m | return_1m | return_2m | return_3m | return_1m_t-1 | return_1m_t-2 | return_1m_t-3 | ||
|---|---|---|---|---|---|---|---|---|---|---|
| ticker | date | |||||||||
| A | 2001-04-30 | -0.140220 | -0.087246 | -0.098192 | 0.269444 | 0.040966 | -0.105747 | -0.146389 | -0.329564 | -0.003653 |
| 2001-05-31 | -0.031008 | -0.076414 | -0.075527 | -0.140220 | 0.044721 | -0.023317 | 0.269444 | -0.146389 | -0.329564 | |
| 2001-06-30 | -0.119692 | -0.097014 | -0.155847 | -0.031008 | -0.087246 | 0.018842 | -0.140220 | 0.269444 | -0.146389 | |
| 2001-07-31 | -0.073750 | -0.173364 | -0.080114 | -0.119692 | -0.076414 | -0.098192 | -0.031008 | -0.140220 | 0.269444 | |
| 2001-08-31 | -0.262264 | -0.083279 | 0.009593 | -0.073750 | -0.097014 | -0.075527 | -0.119692 | -0.031008 | -0.140220 | |
| 2001-09-30 | 0.139130 | 0.181052 | 0.134010 | -0.262264 | -0.173364 | -0.155847 | -0.073750 | -0.119692 | -0.031008 | |
| 2001-10-31 | 0.224517 | 0.131458 | 0.108697 | 0.139130 | -0.083279 | -0.080114 | -0.262264 | -0.073750 | -0.119692 | |
| 2001-11-30 | 0.045471 | 0.054962 | 0.045340 | 0.224517 | 0.181052 | 0.009593 | 0.139130 | -0.262264 | -0.073750 | |
| 2001-12-31 | 0.064539 | 0.045275 | 0.070347 | 0.045471 | 0.131458 | 0.134010 | 0.224517 | 0.139130 | -0.262264 | |
| 2002-01-31 | 0.026359 | 0.073264 | -0.003306 | 0.064539 | 0.054962 | 0.108697 | 0.045471 | 0.224517 | 0.139130 |
In [ ]:
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 30 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 360752 non-null float64
7 SMB 360752 non-null float64
8 HML 360752 non-null float64
9 RMW 360752 non-null float64
10 CMA 360752 non-null float64
11 momentum_2 360752 non-null float64
12 momentum_3 360752 non-null float64
13 momentum_6 360752 non-null float64
14 momentum_9 360752 non-null float64
15 momentum_12 360752 non-null float64
16 momentum_3_12 360752 non-null float64
17 year 360752 non-null int64
18 month 360752 non-null int64
19 return_1m_t-1 358914 non-null float64
20 return_1m_t-2 357076 non-null float64
21 return_1m_t-3 355238 non-null float64
22 return_1m_t-4 353400 non-null float64
23 return_1m_t-5 351562 non-null float64
24 return_1m_t-6 349724 non-null float64
25 target_1m 358914 non-null float64
26 target_2m 357076 non-null float64
27 target_3m 355238 non-null float64
28 target_6m 349724 non-null float64
29 target_12m 338696 non-null float64
dtypes: float64(28), int64(2)
memory usage: 92.1+ MB
Create age proxy¶
We use quintiles of IPO year as a proxy for company age.
In [ ]:
data = (data
.join(pd.qcut(stocks.ipoyear, q=5, labels=list(range(1, 6)))
.astype(float)
.fillna(0)
.astype(int)
.to_frame('age')))
data.age = data.age.fillna(-1)
Create dynamic size proxy¶
We use the marketcap information from the NASDAQ ticker info to create a size proxy.
In [ ]:
stocks.info()
<class 'pandas.core.frame.DataFrame'> Index: 2412 entries, A to ZUMZ Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 marketcap 2407 non-null float64 1 ipoyear 1065 non-null float64 2 sector 2372 non-null object dtypes: float64(2), object(1) memory usage: 139.9+ KB
Market cap information is tied to currrent prices. We create an adjustment factor to have the values reflect lower historical prices for each individual stock:
In [ ]:
size_factor = (monthly_prices
.loc[data.index.get_level_values('date').unique(),
data.index.get_level_values('ticker').unique()]
.sort_index(ascending=False)
.pct_change()
.fillna(0)
.add(1)
.cumprod())
size_factor.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 207 entries, 2018-03-31 to 2001-01-31 Columns: 1838 entries, A to ZUMZ dtypes: float64(1838) memory usage: 2.9 MB
In [ ]:
msize = (size_factor
.mul(stocks
.loc[size_factor.columns, 'marketcap'])).dropna(axis=1, how='all')
Create Size indicator as deciles per period¶
Compute size deciles per month:
In [ ]:
data['msize'] = (msize
.apply(lambda x: pd.qcut(x, q=10, labels=list(range(1, 11)))
.astype(int), axis=1)
.stack()
.swaplevel())
data.msize = data.msize.fillna(-1)
Combine data¶
In [ ]:
data = data.join(stocks[['sector']])
data.sector = data.sector.fillna('Unknown')
In [ ]:
data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 33 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 360752 non-null float64
7 SMB 360752 non-null float64
8 HML 360752 non-null float64
9 RMW 360752 non-null float64
10 CMA 360752 non-null float64
11 momentum_2 360752 non-null float64
12 momentum_3 360752 non-null float64
13 momentum_6 360752 non-null float64
14 momentum_9 360752 non-null float64
15 momentum_12 360752 non-null float64
16 momentum_3_12 360752 non-null float64
17 year 360752 non-null int64
18 month 360752 non-null int64
19 return_1m_t-1 358914 non-null float64
20 return_1m_t-2 357076 non-null float64
21 return_1m_t-3 355238 non-null float64
22 return_1m_t-4 353400 non-null float64
23 return_1m_t-5 351562 non-null float64
24 return_1m_t-6 349724 non-null float64
25 target_1m 358914 non-null float64
26 target_2m 357076 non-null float64
27 target_3m 355238 non-null float64
28 target_6m 349724 non-null float64
29 target_12m 338696 non-null float64
30 age 360752 non-null int64
31 msize 360752 non-null float64
32 sector 360752 non-null object
dtypes: float64(29), int64(3), object(1)
memory usage: 100.4+ MB
Store data¶
We will use the data again in several later chapters, starting in Chapter 7 on Linear Models.
In [ ]:
with pd.HDFStore(DATA_STORE) as store:
store.put('engineered_features', data.sort_index().loc[idx[:, :datetime(2018, 3, 1)], :])
print(store.info())
<class 'pandas.io.pytables.HDFStore'> File path: assets.h5 /engineered_features frame (shape->[358914,33]) /quandl/wiki/prices frame (shape->[15389314,12]) /quandl/wiki/stocks frame (shape->[1,2]) /sp500/fred frame (shape->[2610,1]) /sp500/stocks frame (shape->[503,7]) /sp500/stooq frame (shape->[17700,5]) /us_equities/stocks frame (shape->[6834,6])
Create Dummy variables¶
For most models, we need to encode categorical variables as 'dummies' (one-hot encoding):
In [ ]:
dummy_data = pd.get_dummies(data,
columns=['year','month', 'msize', 'age', 'sector'],
prefix=['year','month', 'msize', 'age', ''],
prefix_sep=['_', '_', '_', '_', ''])
dummy_data = dummy_data.rename(columns={c:c.replace('.0', '') for c in dummy_data.columns})
dummy_data.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 360752 entries, ('A', Timestamp('2001-01-31 00:00:00', freq='M')) to ('ZUMZ', Timestamp('2018-03-31 00:00:00', freq='M'))
Data columns (total 88 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 return_1m 360752 non-null float64
1 return_2m 360752 non-null float64
2 return_3m 360752 non-null float64
3 return_6m 360752 non-null float64
4 return_9m 360752 non-null float64
5 return_12m 360752 non-null float64
6 Mkt-RF 360752 non-null float64
7 SMB 360752 non-null float64
8 HML 360752 non-null float64
9 RMW 360752 non-null float64
10 CMA 360752 non-null float64
11 momentum_2 360752 non-null float64
12 momentum_3 360752 non-null float64
13 momentum_6 360752 non-null float64
14 momentum_9 360752 non-null float64
15 momentum_12 360752 non-null float64
16 momentum_3_12 360752 non-null float64
17 return_1m_t-1 358914 non-null float64
18 return_1m_t-2 357076 non-null float64
19 return_1m_t-3 355238 non-null float64
20 return_1m_t-4 353400 non-null float64
21 return_1m_t-5 351562 non-null float64
22 return_1m_t-6 349724 non-null float64
23 target_1m 358914 non-null float64
24 target_2m 357076 non-null float64
25 target_3m 355238 non-null float64
26 target_6m 349724 non-null float64
27 target_12m 338696 non-null float64
28 year_2001 360752 non-null uint8
29 year_2002 360752 non-null uint8
30 year_2003 360752 non-null uint8
31 year_2004 360752 non-null uint8
32 year_2005 360752 non-null uint8
33 year_2006 360752 non-null uint8
34 year_2007 360752 non-null uint8
35 year_2008 360752 non-null uint8
36 year_2009 360752 non-null uint8
37 year_2010 360752 non-null uint8
38 year_2011 360752 non-null uint8
39 year_2012 360752 non-null uint8
40 year_2013 360752 non-null uint8
41 year_2014 360752 non-null uint8
42 year_2015 360752 non-null uint8
43 year_2016 360752 non-null uint8
44 year_2017 360752 non-null uint8
45 year_2018 360752 non-null uint8
46 month_1 360752 non-null uint8
47 month_2 360752 non-null uint8
48 month_3 360752 non-null uint8
49 month_4 360752 non-null uint8
50 month_5 360752 non-null uint8
51 month_6 360752 non-null uint8
52 month_7 360752 non-null uint8
53 month_8 360752 non-null uint8
54 month_9 360752 non-null uint8
55 month_10 360752 non-null uint8
56 month_11 360752 non-null uint8
57 month_12 360752 non-null uint8
58 msize_-1 360752 non-null uint8
59 msize_1 360752 non-null uint8
60 msize_2 360752 non-null uint8
61 msize_3 360752 non-null uint8
62 msize_4 360752 non-null uint8
63 msize_5 360752 non-null uint8
64 msize_6 360752 non-null uint8
65 msize_7 360752 non-null uint8
66 msize_8 360752 non-null uint8
67 msize_9 360752 non-null uint8
68 msize_10 360752 non-null uint8
69 age_0 360752 non-null uint8
70 age_1 360752 non-null uint8
71 age_2 360752 non-null uint8
72 age_3 360752 non-null uint8
73 age_4 360752 non-null uint8
74 age_5 360752 non-null uint8
75 Basic Industries 360752 non-null uint8
76 Capital Goods 360752 non-null uint8
77 Consumer Durables 360752 non-null uint8
78 Consumer Non-Durables 360752 non-null uint8
79 Consumer Services 360752 non-null uint8
80 Energy 360752 non-null uint8
81 Finance 360752 non-null uint8
82 Health Care 360752 non-null uint8
83 Miscellaneous 360752 non-null uint8
84 Public Utilities 360752 non-null uint8
85 Technology 360752 non-null uint8
86 Transportation 360752 non-null uint8
87 Unknown 360752 non-null uint8
dtypes: float64(28), uint8(60)
memory usage: 107.2+ MB
TA-Lib를 사용해 기술적 알파 팩터를 만드는 방법¶
02_how_to_use_talib.ipynb¶
How to use TA-Lib to generate Alpha Factors¶
- TA-Lib를 사용해 기술적 알파 팩터를 만드는 방법
Imports & Settings¶
In [ ]:
import warnings
warnings.filterwarnings('ignore')
In [ ]:
# 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
--2023-06-18 09:19:38-- 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] --2023-06-18 09:19:38-- 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] --2023-06-18 09:19:38-- 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 3.12MB/s in 0.4s 2023-06-18 09:19:38 (3.12 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 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tables/table_d.c -fPIC -DPIC -o .libs/libta_abstract_la-table_d.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_d.lo -MD -MP -MF .deps/libta_abstract_la-table_d.Tpo -c tables/table_d.c -o libta_abstract_la-table_d.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_d.Tpo .deps/libta_abstract_la-table_d.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_e.lo -MD -MP -MF .deps/libta_abstract_la-table_e.Tpo -c -o libta_abstract_la-table_e.lo `test -f 'tables/table_e.c' || echo './'`tables/table_e.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_e.lo -MD -MP -MF .deps/libta_abstract_la-table_e.Tpo -c tables/table_e.c -fPIC -DPIC -o .libs/libta_abstract_la-table_e.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_e.lo -MD -MP -MF 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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_g.lo -MD -MP -MF .deps/libta_abstract_la-table_g.Tpo -c -o libta_abstract_la-table_g.lo `test -f 'tables/table_g.c' || echo './'`tables/table_g.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_g.lo -MD -MP -MF .deps/libta_abstract_la-table_g.Tpo -c tables/table_g.c -fPIC -DPIC -o .libs/libta_abstract_la-table_g.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_g.lo -MD -MP -MF .deps/libta_abstract_la-table_g.Tpo -c tables/table_g.c -o libta_abstract_la-table_g.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_g.Tpo .deps/libta_abstract_la-table_g.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_h.lo -MD -MP -MF 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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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tables/table_u.c -fPIC -DPIC -o .libs/libta_abstract_la-table_u.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_u.lo -MD -MP -MF .deps/libta_abstract_la-table_u.Tpo -c tables/table_u.c -o libta_abstract_la-table_u.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_u.Tpo .deps/libta_abstract_la-table_u.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_v.lo -MD -MP -MF .deps/libta_abstract_la-table_v.Tpo -c -o libta_abstract_la-table_v.lo `test -f 'tables/table_v.c' || echo './'`tables/table_v.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_v.lo -MD -MP -MF .deps/libta_abstract_la-table_v.Tpo -c tables/table_v.c -fPIC -DPIC -o .libs/libta_abstract_la-table_v.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_v.lo -MD -MP -MF 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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 .deps/libta_abstract_la-table_y.Tpo -c -o libta_abstract_la-table_y.lo `test -f 'tables/table_y.c' || echo './'`tables/table_y.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_y.lo -MD -MP -MF .deps/libta_abstract_la-table_y.Tpo -c tables/table_y.c -fPIC -DPIC -o .libs/libta_abstract_la-table_y.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -Iframes/ -g -O2 -MT libta_abstract_la-table_y.lo -MD -MP -MF .deps/libta_abstract_la-table_y.Tpo -c tables/table_y.c -o libta_abstract_la-table_y.o >/dev/null 2>&1 mv -f .deps/libta_abstract_la-table_y.Tpo .deps/libta_abstract_la-table_y.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_z.lo -MD -MP -MF .deps/libta_abstract_la-table_z.Tpo -c -o libta_abstract_la-table_z.lo `test -f 'tables/table_z.c' || echo './'`tables/table_z.c 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 -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 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--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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-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 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-I../ta_common/ -g -O2 -MT ta_VAR.lo -MD -MP -MF .deps/ta_VAR.Tpo -c ta_VAR.c -fPIC -DPIC -o .libs/ta_VAR.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_VAR.lo -MD -MP -MF .deps/ta_VAR.Tpo -c ta_VAR.c -o ta_VAR.o >/dev/null 2>&1 mv -f .deps/ta_VAR.Tpo .deps/ta_VAR.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WCLPRICE.lo -MD -MP -MF .deps/ta_WCLPRICE.Tpo -c -o ta_WCLPRICE.lo ta_WCLPRICE.c gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WCLPRICE.lo -MD -MP -MF .deps/ta_WCLPRICE.Tpo -c ta_WCLPRICE.c -fPIC -DPIC -o .libs/ta_WCLPRICE.o gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT ta_WCLPRICE.lo -MD -MP -MF .deps/ta_WCLPRICE.Tpo -c ta_WCLPRICE.c -o ta_WCLPRICE.o >/dev/null 2>&1 mv -f .deps/ta_WCLPRICE.Tpo .deps/ta_WCLPRICE.Plo /bin/bash ../../libtool --tag=CC --mode=compile gcc -DHAVE_CONFIG_H -I. -I../../include -I../ta_common/ -g -O2 -MT 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_x.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_i.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_t.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_w.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_b.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-table_l.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_e.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_u.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_p.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_h.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_a.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_s.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_c.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_o.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_m.o .libs/libta_lib.lax/libta_abstract.a/libta_abstract_la-table_y.o .libs/libta_lib.lax/libta_func.a/ta_DX.o .libs/libta_lib.lax/libta_func.a/ta_PPO.o .libs/libta_lib.lax/libta_func.a/ta_ATAN.o .libs/libta_lib.lax/libta_func.a/ta_BOP.o .libs/libta_lib.lax/libta_func.a/ta_T3.o .libs/libta_lib.lax/libta_func.a/ta_CDLHARAMICROSS.o .libs/libta_lib.lax/libta_func.a/ta_ADXR.o .libs/libta_lib.lax/libta_func.a/ta_CDLMARUBOZU.o .libs/libta_lib.lax/libta_func.a/ta_ROC.o .libs/libta_lib.lax/libta_func.a/ta_MININDEX.o .libs/libta_lib.lax/libta_func.a/ta_SUM.o .libs/libta_lib.lax/libta_func.a/ta_APO.o .libs/libta_lib.lax/libta_func.a/ta_CMO.o .libs/libta_lib.lax/libta_func.a/ta_CDLONNECK.o .libs/libta_lib.lax/libta_func.a/ta_ADOSC.o .libs/libta_lib.lax/libta_func.a/ta_STOCHF.o .libs/libta_lib.lax/libta_func.a/ta_CDLHIKKAKE.o .libs/libta_lib.lax/libta_func.a/ta_SQRT.o .libs/libta_lib.lax/libta_func.a/ta_PLUS_DM.o .libs/libta_lib.lax/libta_func.a/ta_CEIL.o .libs/libta_lib.lax/libta_func.a/ta_CDLRICKSHAWMAN.o .libs/libta_lib.lax/libta_func.a/ta_TRANGE.o .libs/libta_lib.lax/libta_func.a/ta_BETA.o .libs/libta_lib.lax/libta_func.a/ta_CDL3BLACKCROWS.o .libs/libta_lib.lax/libta_func.a/ta_CDLDOJISTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLHIKKAKEMOD.o .libs/libta_lib.lax/libta_func.a/ta_SINH.o .libs/libta_lib.lax/libta_func.a/ta_SAREXT.o .libs/libta_lib.lax/libta_func.a/ta_CDLHARAMI.o .libs/libta_lib.lax/libta_func.a/ta_ADX.o .libs/libta_lib.lax/libta_func.a/ta_CDLTRISTAR.o .libs/libta_lib.lax/libta_func.a/ta_CDLMATHOLD.o .libs/libta_lib.lax/libta_func.a/ta_CDLXSIDEGAP3METHODS.o .libs/libta_lib.lax/libta_func.a/ta_MOM.o .libs/libta_lib.lax/libta_func.a/ta_MEDPRICE.o .libs/libta_lib.lax/libta_func.a/ta_CDLHAMMER.o .libs/libta_lib.lax/libta_func.a/ta_STDDEV.o .libs/libta_lib.lax/libta_func.a/ta_AD.o .libs/libta_lib.lax/libta_func.a/ta_ROCR.o .libs/libta_lib.lax/libta_func.a/ta_MACDEXT.o .libs/libta_lib.lax/libta_func.a/ta_HT_TRENDLINE.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG_ANGLE.o .libs/libta_lib.lax/libta_func.a/ta_CDLCLOSINGMARUBOZU.o .libs/libta_lib.lax/libta_func.a/ta_CDLDRAGONFLYDOJI.o .libs/libta_lib.lax/libta_func.a/ta_CDLSTALLEDPATTERN.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_HT_PHASOR.o .libs/libta_lib.lax/libta_func.a/ta_CDLUPSIDEGAP2CROWS.o .libs/libta_lib.lax/libta_func.a/ta_MINUS_DI.o 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.libs/libta_lib.lax/libta_func.a/ta_CDLHOMINGPIGEON.o .libs/libta_lib.lax/libta_func.a/ta_STOCHRSI.o .libs/libta_lib.lax/libta_func.a/ta_HT_DCPHASE.o .libs/libta_lib.lax/libta_func.a/ta_MAX.o .libs/libta_lib.lax/libta_func.a/ta_CDL2CROWS.o .libs/libta_lib.lax/libta_func.a/ta_TSF.o .libs/libta_lib.lax/libta_func.a/ta_CDLHANGINGMAN.o .libs/libta_lib.lax/libta_func.a/ta_MAVP.o .libs/libta_lib.lax/libta_func.a/ta_CDLGRAVESTONEDOJI.o .libs/libta_lib.lax/libta_func.a/ta_CDLBELTHOLD.o .libs/libta_lib.lax/libta_func.a/ta_EMA.o .libs/libta_lib.lax/libta_func.a/ta_MIDPOINT.o .libs/libta_lib.lax/libta_func.a/ta_KAMA.o .libs/libta_lib.lax/libta_func.a/ta_LN.o .libs/libta_lib.lax/libta_func.a/ta_TYPPRICE.o .libs/libta_lib.lax/libta_func.a/ta_CDLBREAKAWAY.o .libs/libta_lib.lax/libta_func.a/ta_CDLMATCHINGLOW.o .libs/libta_lib.lax/libta_func.a/ta_MAMA.o .libs/libta_lib.lax/libta_func.a/ta_CDLPIERCING.o .libs/libta_lib.lax/libta_func.a/ta_LINEARREG_INTERCEPT.o .libs/libta_lib.lax/libta_func.a/ta_NATR.o .libs/libta_lib.lax/libta_func.a/ta_ROCP.o .libs/libta_lib.lax/libta_func.a/ta_AROON.o .libs/libta_lib.lax/libta_func.a/ta_CCI.o .libs/libta_lib.lax/libta_func.a/ta_HT_DCPERIOD.o .libs/libta_lib.lax/libta_func.a/ta_TANH.o .libs/libta_lib.lax/libta_func.a/ta_CDLINNECK.o .libs/libta_lib.lax/libta_func.a/ta_WILLR.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 6.2 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=2238946 sha256=b5dd38952f911f9a1f90407ed8f49917161fa0c616b87662aa7872474ef0434e 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 [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from talib import RSI, BBANDS, MACD
In [ ]:
sns.set_style('whitegrid')
idx = pd.IndexSlice
Get Data¶
The assets.h5 store can be generated using the the notebook create_datasets in the data directory in the root directory of this repo for instruction to download the following dataset.
Set data store location:
In [ ]:
DATA_STORE = 'assets.h5'
We load the AAPL stock price for the 2007-10 using pd.IndexSlice to perform a slice operation on the pd.MultiIndex, select the adjusted close price and unpivot the column to convert the DataFrame to wide format with tickers in the columns and timestamps in the rows:
In [ ]:
!unzip /content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/WIKI_PRICES.zip
!mv WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv wiki_prices.csv
Archive: /content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/WIKI_PRICES.zip inflating: WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv
In [ ]:
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 [ ]:
with pd.HDFStore(DATA_STORE) as store:
data = (store['quandl/wiki/prices']
.loc[idx['2007':'2010', 'AAPL'],
['adj_open', 'adj_high', 'adj_low', 'adj_close', 'adj_volume']]
.unstack('ticker')
.swaplevel(axis=1)
.loc[:, 'AAPL']
.rename(columns=lambda x: x.replace('adj_', '')))
In [ ]:
data.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 1008 entries, 2007-01-03 to 2010-12-31 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 open 1008 non-null float64 1 high 1008 non-null float64 2 low 1008 non-null float64 3 close 1008 non-null float64 4 volume 1008 non-null float64 dtypes: float64(5) memory usage: 47.2 KB
Compute Bollinger Bands¶
In [ ]:
up, mid, low = BBANDS(data.close, timeperiod=21, nbdevup=2, nbdevdn=2, matype=0)
Compute Relative Strength Index¶
In [ ]:
rsi = RSI(data.close, timeperiod=14)
Moving Average Convergence/Divergence¶
MACD는 두 지수 이동 평균(EMA)인 장기 및 단기 기간 사이의 차이를 계산합니다.
ta-lib의 MACD 지표 구현은 네 가지 입력값을 갖습니다:
- 종가(close price)
fastperiod: 단기 EMA 기간slowperiod: 장기 EMA 기간signalperiod: MACD 자체의 EMA 기간
세 가지 출력값을 갖습니다:
macd: 빠른 EMA와 느린 EMA 사이의 차이입니다.macdsignal:signalperiod기간으로 MACD 값의 EMA입니다.macdhist:macd와macdsignal사이의 차이를 계산합니다.
In [ ]:
macd, macdsignal, macdhist = MACD(data.close, fastperiod=12, slowperiod=26, signalperiod=9)
In [ ]:
macd_data = pd.DataFrame({'AAPL': data.close, 'MACD': macd, 'MACD Signal': macdsignal, 'MACD History': macdhist})
fig, axes= plt.subplots(nrows=2, figsize=(15, 8))
macd_data.AAPL.plot(ax=axes[0])
macd_data.drop('AAPL', axis=1).plot(ax=axes[1])
fig.tight_layout()
sns.despine();
Plot Result¶
In [ ]:
data = pd.DataFrame({'AAPL': data.close, 'BB Up': up, 'BB Mid': mid, 'BB down': low, 'RSI': rsi, 'MACD': macd})
In [ ]:
fig, axes= plt.subplots(nrows=3, figsize=(15, 10), sharex=True)
data.drop(['RSI', 'MACD'], axis=1).plot(ax=axes[0], lw=1, title='Bollinger Bands')
data['RSI'].plot(ax=axes[1], lw=1, title='Relative Strength Index')
axes[1].axhline(70, lw=1, ls='--', c='k')
axes[1].axhline(30, lw=1, ls='--', c='k')
data.MACD.plot(ax=axes[2], lw=1, title='Moving Average Convergence/Divergence', rot=0)
axes[2].set_xlabel('')
fig.tight_layout()
sns.despine();
In [ ]:
칼만 필터를 사용한 알파 팩터의 잡음 제거¶
칼만 필터의 작동 원리¶
칼만 필터: 시계열과 같은 순차적 데이터에 대한 동적 선형 모델로 새로운 정보가 도달하면 이에 대해 적응하는 모델
- 확률 모델을 기반으로 새로운 데이터를 시계열의 현재 값 추정에 반영한다.
- 시간 경과에 따른 측정치 z가 주어졌을 때 상태 변수 x의 사후 분포를 ㅈㄴ파하는 베이지안 접근법을 취한다.
실제 시간에서 수집할 수 있는 관측치 시퀀스에서 은닉 상태를 복원할 때 알고리듬은 다음 두 스텝을 반복한다.
- 예측 스텝: 프로세스의 현재 상태를 추정한다.
- 측정 스텝: 잡음이 섞인 관측을 사용해 양 스텝에서 정보를 더 확실한 추정치에 더 높은 가중치를 주는 식으로 평균해 추정치를 업데이트한다.
03_kalman_filter_and_wavelets.ipynb¶
How to use the Kalman Filter and Wavelets¶
- pykalman을 이용한 칼만 필터의 적용법
Imports & Settings¶
In [ ]:
import warnings
warnings.filterwarnings('ignore')
In [ ]:
!pip install pykalman
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting pykalman
Downloading pykalman-0.9.5.tar.gz (228 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 228.9/228.9 kB 6.5 MB/s eta 0:00:00
Preparing metadata (setup.py) ... done
Building wheels for collected packages: pykalman
Building wheel for pykalman (setup.py) ... done
Created wheel for pykalman: filename=pykalman-0.9.5-py3-none-any.whl size=48442 sha256=72914e2aca7e9407bfb897637cbf31c6468f479a7d7bf45dbdfb9ea271129e69
Stored in directory: /root/.cache/pip/wheels/76/33/ef/5f332226e13a5089c6dd4b01cc2bcb59491d18f955fa2d3807
Successfully built pykalman
Installing collected packages: pykalman
Successfully installed pykalman-0.9.5
In [ ]:
%matplotlib inline
from datetime import datetime
import itertools
import pandas as pd
import pandas_datareader.data as web
from pykalman import KalmanFilter
import pywt
import matplotlib.pyplot as plt
import seaborn as sns
In [ ]:
warnings.filterwarnings('ignore')
sns.set_style('whitegrid')
idx = pd.IndexSlice
Get Data¶
The assets.h5 store can be generated using the the notebook create_datasets in the data directory in the root directory of this repo for instruction to download the following dataset.
We load the Quandl stock price datasets covering the US equity markets 2000-18 using pd.IndexSlice to perform a slice operation on the pd.MultiIndex, select the adjusted close price and unpivot the column to convert the DataFrame to wide format with tickers in the columns and timestamps in the rows:
Set data store location:
In [ ]:
DATA_STORE = 'assets.h5'
In [ ]:
df = web.DataReader(name='SP500', data_source='fred', start=2009).squeeze().to_frame('close')
print(df.info())
with pd.HDFStore(DATA_STORE) as store:
store.put('sp500/fred', df)
sp500_stooq = (pd.read_csv('/content/drive/MyDrive/스터디/금융공학_퀀트_스터디/data/^spx_d.csv', index_col=0,
parse_dates=True).loc['1950':'2019'].rename(columns=str.lower))
print(sp500_stooq.info())
with pd.HDFStore(DATA_STORE) as store:
store.put('sp500/stooq', sp500_stooq)
url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
df = pd.read_html(url, header=0)[0]
df.columns = ['ticker', 'name', 'gics_sector', 'gics_sub_industry',
'location', 'first_added', 'cik', 'founded']
df = df.set_index('ticker')
# sec_filings이 사라짐
with pd.HDFStore(DATA_STORE) as store:
store.put('sp500/stocks', df)
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 2610 entries, 2013-06-17 to 2023-06-16 Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 close 2519 non-null float64 dtypes: float64(1) memory usage: 40.8 KB None <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 17700 entries, 1950-01-03 to 2019-12-31 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 open 17700 non-null float64 1 high 17700 non-null float64 2 low 17700 non-null float64 3 close 17700 non-null float64 4 volume 17700 non-null float64 dtypes: float64(5) memory usage: 829.7 KB None
In [ ]:
with pd.HDFStore(DATA_STORE) as store:
sp500 = store['sp500/stooq'].loc['2009': '2010', 'close']
Kalman Filter¶
Configuration¶
In [ ]:
kf = KalmanFilter(transition_matrices = [1],
observation_matrices = [1],
initial_state_mean = 0,
initial_state_covariance = 1,
observation_covariance=1,
transition_covariance=.01)
Estimate the hidden state¶
In [ ]:
state_means, _ = kf.filter(sp500)
Compare with moving average¶
In [ ]:
sp500_smoothed = sp500.to_frame('close')
sp500_smoothed['Kalman Filter'] = state_means
for months in [1,2,3]:
sp500_smoothed[f'MA ({months}m)'] = sp500.rolling(window=months*21).mean()
ax = sp500_smoothed.plot(title='Kalman Filter vs Moving Average', figsize=(14,6), lw=1, rot=0)
ax.set_xlabel('')
ax.set_ylabel('S&P 500')
plt.tight_layout()
sns.despine();
Wavelets¶
- 웨이블릿을 이용한 잡음 데이터의 전처리 방법
In [ ]:
wavelet = pywt.Wavelet('db6')
phi, psi, x = wavelet.wavefun(level=5)
df = pd.DataFrame({'$\phi$': phi, '$\psi$': psi}, index=x)
df.plot(title='Daubechies', subplots=True, layout=(1, 2), figsize=(14, 4), lw=2, rot=0)
plt.tight_layout()
sns.despine();
In [ ]:
plot_data = [('db', (4, 3)),
('sym', (4, 3)),
('coif', (3, 2))]
for family, (rows, cols) in plot_data:
fig = plt.figure(figsize=(24, 12))
fig.subplots_adjust(hspace=0.2, wspace=0.2, bottom=.02, left=.06,
right=.97, top=.94)
colors = itertools.cycle('bgrcmyk')
wnames = pywt.wavelist(family)
i = iter(wnames)
for col in range(cols):
for row in range(rows):
try:
wavelet = pywt.Wavelet(next(i))
except StopIteration:
break
phi, psi, x = wavelet.wavefun(level=5)
color = next(colors)
ax = fig.add_subplot(rows, 2 * cols, 1 + 2 * (col + row * cols))
ax.set_title(wavelet.name + " phi")
ax.plot(x, phi, color, lw=1)
ax.set_xlim(min(x), max(x))
ax = fig.add_subplot(rows, 2*cols, 1 + 2*(col + row*cols) + 1)
ax.set_title(wavelet.name + " psi")
ax.plot(x, psi, color, lw=1)
ax.set_xlim(min(x), max(x))
sns.despine()
for family, (rows, cols) in [('bior', (4, 3)), ('rbio', (4, 3))]:
fig = plt.figure(figsize=(24, 12))
fig.subplots_adjust(hspace=0.5, wspace=0.2, bottom=.02, left=.06,
right=.97, top=.94)
colors = itertools.cycle('bgrcmyk')
wnames = pywt.wavelist(family)
i = iter(wnames)
for col in range(cols):
for row in range(rows):
try:
wavelet = pywt.Wavelet(next(i))
except StopIteration:
break
phi, psi, phi_r, psi_r, x = wavelet.wavefun(level=5)
row *= 2
color = next(colors)
ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols))
ax.set_title(wavelet.name + " phi")
ax.plot(x, phi, color, lw=1)
ax.set_xlim(min(x), max(x))
ax = fig.add_subplot(2*rows, 2*cols, 2*(1 + col + row*cols))
ax.set_title(wavelet.name + " psi")
ax.plot(x, psi, color, lw=1)
ax.set_xlim(min(x), max(x))
row += 1
ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols))
ax.set_title(wavelet.name + " phi_r")
ax.plot(x, phi_r, color, lw=1)
ax.set_xlim(min(x), max(x))
ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols) + 1)
ax.set_title(wavelet.name + " psi_r")
ax.plot(x, psi_r, color, lw=1)
ax.set_xlim(min(x), max(x))
sns.despine()
plt.show()
Available Wavelet Families¶
In [ ]:
pywt.families(short=False)
Out[ ]:
['Haar', 'Daubechies', 'Symlets', 'Coiflets', 'Biorthogonal', 'Reverse biorthogonal', 'Discrete Meyer (FIR Approximation)', 'Gaussian', 'Mexican hat wavelet', 'Morlet wavelet', 'Complex Gaussian wavelets', 'Shannon wavelets', 'Frequency B-Spline wavelets', 'Complex Morlet wavelets']
Smoothing the S&P 500¶
In [ ]:
signal = (pd.read_hdf(DATA_STORE, 'sp500/stooq')
.loc['2008': '2009']
.close.pct_change()
.dropna())
fig, axes = plt.subplots(ncols=2, figsize=(14, 5))
wavelet = "db6"
for i, scale in enumerate([.1, .5]):
coefficients = pywt.wavedec(signal, wavelet, mode='per')
coefficients[1:] = [pywt.threshold(i, value=scale*signal.max(), mode='soft') for i in coefficients[1:]]
reconstructed_signal = pywt.waverec(coefficients, wavelet, mode='per')
signal.plot(color="b", alpha=0.5, label='original signal', lw=2,
title=f'Threshold Scale: {scale:.1f}', ax=axes[i])
pd.Series(reconstructed_signal, index=signal.index).plot(c='k', label='DWT smoothing}', linewidth=1, ax=axes[i])
axes[i].legend()
fig.tight_layout()
sns.despine();
In [ ]:
신호에서 트레이딩으로: 백테스트를 위한 집라인¶
단일 팩터 전략을 백테스트 하는 방법¶
04_single_factor_zipline.ipynb¶
Zipline Backtest with Single Factor¶
- 시장 데이터에서 산출한 단일 알파 팩터
This notebook develops and test a simple mean-reversion factor that measures how much recent performance has deviated from the historical average. Short-term reversal is a common strategy that takes advantage of the weakly predictive pattern that stock price increases are likely to mean-revert back down over horizons from less than a minute to one month.
Imports & Settings¶
In [ ]:
import warnings
warnings.filterwarnings('ignore')
In [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
In [ ]:
sns.set_style('whitegrid')
먼저, 오프라인 환경에서 zipline alpha factor 연구 워크플로우를 설명하겠습니다. 특히, 최근 성과가 과거 평균과 얼마나 벗어났는지를 측정하는 간단한 평균회귀(factor)를 개발하고 테스트할 것입니다.
단기 회귀는 주식 가격이 일분부터 한 달까지의 기간 동안 하락하는 경향을 가지고 있다는 약한 예측력 패턴을 활용하는 일반적인 전략입니다.
이를 위해 해당 factor는 지난 한 해 동안의 이동 월간 수익률에 대한 z-score를 마지막 월간 수익률과 비교합니다. 이 시점에서 주문을 넣지 않고, 단지 CustomFactor의 구현과 시뮬레이션 중에 결과를 기록하는 방법을 설명하기 위한 것입니다.
기본적인 설정 이후, MeanReversion은 CustomFactor를 상속하고 compute() 메서드를 정의합니다. 기본 입력값으로는 기본적으로 1년 기간의 월간 수익률로 설정되어 있으므로, monthly_return 변수는 주어진 날짜의 Quandl 데이터셋의 각 보안에 대해 252개의 행과 1개의 열을 갖게 됩니다.
compute_factors() 메서드는 MeanReversion 팩터 인스턴스를 생성하고, long, short, 그리고 ranking 파이프라인 컬럼을 생성합니다. 이전 두 컬럼은 주문을 넣는 데 사용될 수 있는 불리언 값이며, 후자는 전체적인 순위를 반영하여 전체적인 팩터 성능을 평가합니다. 또한, 보다 유동성이 높은 주식에 대해서만 계산을 제한하기 위해 내장된 AverageDollarVolume 팩터를 사용합니다.
결과를 통해 long과 short 주문을 넣을 수 있습니다. 다음 장에서는 리밸런싱 기간을 선택하고 새로운 신호가 도착할 때 포트폴리오 보유량을 조정하여 포트폴리오를 구축하는 방법에 대해 알아보겠습니다.
initialize()메서드는 compute_factors() 파이프라인을 등록하며, before_trading_start() 메서드는 파이프라인이 매일 실행되도록 보장합니다.record()함수는 파이프라인의 순위 컬럼과 현재 자산 가격을run_algorithm()함수에 의해 반환된 성과 DataFrame에 추가합니다.
Run using jupyter notebook extension
In [ ]:
!wget https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files/libta-lib0_0.4.0-oneiric1_amd64.deb -qO libta.deb
!wget https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files/ta-lib0-dev_0.4.0-oneiric1_amd64.deb -qO ta.deb
!dpkg -i libta.deb ta.deb
!pip install ta-lib
import talib
!pip install zipline-reloaded
!pip install pyfolio
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In [ ]:
import os
os.environ['QUANDL_API_KEY'] = 'Your Key'
In [ ]:
%load_ext zipline
Using the quandl bundle instead of the default quantopian-quandl because the latter has a bug that requires (manually) fixing the SQL database. If you have a file with benchmark returns you can provide this instead of --no-benchmark (see docs).
In [ ]:
# !zipline ingest --bundle quandl
In [ ]:
%%writefile algorithm.py
from zipline.api import (
attach_pipeline,
date_rules,
time_rules,
order_target_percent,
pipeline_output,
record,
schedule_function,
get_open_orders,
calendars
)
from zipline.finance import commission, slippage
from zipline.pipeline import Pipeline, CustomFactor
from zipline.pipeline.factors import Returns, AverageDollarVolume
import numpy as np
import pandas as pd
MONTH = 21
YEAR = 12 * MONTH
N_LONGS = N_SHORTS = 25
VOL_SCREEN = 1000
class MeanReversion(CustomFactor):
"""Compute ratio of latest monthly return to 12m average,
normalized by std dev of monthly returns"""
inputs = [Returns(window_length=MONTH)]
window_length = YEAR
def compute(self, today, assets, out, monthly_returns):
df = pd.DataFrame(monthly_returns)
out[:] = df.iloc[-1].sub(df.mean()).div(df.std())
def compute_factors():
"""Create factor pipeline incl. mean reversion,
filtered by 30d Dollar Volume; capture factor ranks"""
mean_reversion = MeanReversion()
dollar_volume = AverageDollarVolume(window_length=30)
return Pipeline(columns={'longs': mean_reversion.bottom(N_LONGS),
'shorts': mean_reversion.top(N_SHORTS),
'ranking': mean_reversion.rank(ascending=False)},
screen=dollar_volume.top(VOL_SCREEN))
def exec_trades(data, assets, target_percent):
"""Place orders for assets using target portfolio percentage"""
for asset in assets:
if data.can_trade(asset) and not get_open_orders(asset):
order_target_percent(asset, target_percent)
def rebalance(context, data):
"""Compute long, short and obsolete holdings; place trade orders"""
factor_data = context.factor_data
record(factor_data=factor_data.ranking)
assets = factor_data.index
record(prices=data.current(assets, 'price'))
longs = assets[factor_data.longs]
shorts = assets[factor_data.shorts]
divest = set(context.portfolio.positions.keys()) - set(longs.union(shorts))
exec_trades(data, assets=divest, target_percent=0)
exec_trades(data, assets=longs, target_percent=1 / N_LONGS)
exec_trades(data, assets=shorts, target_percent=-1 / N_SHORTS)
def initialize(context):
"""Setup: register pipeline, schedule rebalancing,
and set trading params"""
attach_pipeline(compute_factors(), 'factor_pipeline')
schedule_function(rebalance,
date_rules.week_start(),
time_rules.market_open(),
calendar=calendars.US_EQUITIES)
context.set_commission(commission.PerShare(cost=.01, min_trade_cost=0))
context.set_slippage(slippage.VolumeShareSlippage())
def before_trading_start(context, data):
"""Run factor pipeline"""
context.factor_data = pipeline_output('factor_pipeline')
Writing algorithm.py
In [ ]:
!zipline run -f algorithm.py --start 2015-01-01 --end 2018-01-01 --output single_factor.pickle --no-benchmark --bundle quandl
[2023-06-18T14:45:32+0000-INFO][zipline.finance.metrics.tracker] Simulated 755 trading days first open: 2015-01-02 14:30:00+00:00 last close: 2017-12-29 21:00:00+00:00
Inspect Results¶
We can get the result DataFrame using _ which captures the last cell output (only works when you run it right after the above cell)
In [ ]:
import pandas as pd
import pickle
# single_factor.pickle 파일을 열고 데이터를 로드
with open('single_factor.pickle', 'rb') as f:
data = pickle.load(f)
# DataFrame으로 변환
result = pd.DataFrame(data)
In [ ]:
result.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 755 entries, 2015-01-02 21:00:00+00:00 to 2017-12-29 21:00:00+00:00 Data columns (total 39 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 period_open 755 non-null datetime64[ns, UTC] 1 period_close 755 non-null datetime64[ns, UTC] 2 short_value 755 non-null float64 3 long_exposure 755 non-null float64 4 pnl 755 non-null float64 5 short_exposure 755 non-null float64 6 capital_used 755 non-null float64 7 orders 755 non-null object 8 transactions 755 non-null object 9 gross_leverage 755 non-null float64 10 positions 755 non-null object 11 net_leverage 755 non-null float64 12 starting_exposure 755 non-null float64 13 returns 755 non-null float64 14 ending_exposure 755 non-null float64 15 starting_value 755 non-null float64 16 ending_value 755 non-null float64 17 starting_cash 755 non-null float64 18 ending_cash 755 non-null float64 19 portfolio_value 755 non-null float64 20 longs_count 755 non-null int64 21 shorts_count 755 non-null int64 22 long_value 755 non-null float64 23 algo_volatility 754 non-null float64 24 benchmark_period_return 755 non-null float64 25 benchmark_volatility 754 non-null float64 26 algorithm_period_return 755 non-null float64 27 alpha 0 non-null object 28 beta 0 non-null object 29 sharpe 753 non-null float64 30 sortino 753 non-null float64 31 max_drawdown 755 non-null float64 32 max_leverage 755 non-null float64 33 excess_return 755 non-null float64 34 treasury_period_return 755 non-null float64 35 trading_days 755 non-null int64 36 period_label 755 non-null object 37 factor_data 754 non-null object 38 prices 754 non-null object dtypes: datetime64[ns, UTC](2), float64(26), int64(3), object(8) memory usage: 235.9+ KB
In [ ]:
fig, axes = plt.subplots(nrows=2, figsize=(14,6))
result.algorithm_period_return.plot(ax=axes[0], title='Cumulative Return')
result.algo_volatility.plot(ax=axes[1], title='Volatility')
sns.despine()
fig.tight_layout();
다양한 데이터 소스의 팩터 결합¶
alphalens_tutorial_on_quantopian.ipynb¶
알파렌즈를 사용한 신호와 잡음 분리¶
미래 수익률과 팩터 분위수의 생성¶
06_performance_eval_alphalens.ipynb¶
Separating signal and noise – how to use alphalens¶
Quantopian은 알파렌즈(Alphalens)라는 Python 라이브러리를 개방 소스로 공개하였으며, 이는 예측 주식 팩터의 성능 분석에 잘 통합되는 zipline 백테스팅 라이브러리와 포트폴리오 성과 및 리스크 분석 라이브러리인 pyfolio와 함께 사용됩니다. 이에 대해서는 다음 장에서 살펴보겠습니다. 알파렌즈는 다음과 같은 측면에서 알파 팩터의 예측력 분석을 용이하게 해줍니다:
- 신호와 후속 수익의 상관관계
- (부분적인) 신호를 기반으로 한 동일 가중치 또는 팩터 가중치 포트폴리오의 수익성
- 팩터의 턴오버를 통해 잠재적인 거래 비용을 나타냄
- 특정 이벤트 기간 동안의 팩터 성능
- 섹터별로 분류된 이전 분석 결과
해당 분석은 티어시트(tearsheet) 또는 개별 계산 및 그래프를 통해 수행할 수 있습니다.
이 노트북은
conda환경backtest을 요구합니다. 최신 Docker 이미지를 실행하거나 환경을 설정하는 대체 방법에 대한 설치 지침을 보려면 설치 지침을 참조하십시오.
Imports & Settings¶
In [ ]:
import warnings
warnings.filterwarnings('ignore')
In [ ]:
%matplotlib inline
import re
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.performance import *
from alphalens.plotting import *
from alphalens.tears import *
import seaborn as sns
import matplotlib.pyplot as plt
In [ ]:
sns.set_style('whitegrid')
Creating forward returns and factor quantiles¶
- 미래 수익률과 팩터 분위수의 생성
알파렌즈를 활용하기 위해서는 주어진 보유 기간 동안 자산에 투자하여 얻은 전방 수익률과 같이 자산 집합을 위한 신호를 제공해야 합니다.
이 노트북은 이 디렉토리에 있는
single_factor_zipline.ipynb노트북에서 생성된 결과인single_factor.pickle파일을 사용합니다.
우리는 다음과 같이 single_factor.pickle 파일에서 가격을 복구할 것입니다 (factor_data도 동일하게)
single_factor.pickle 파일을 읽어들이고, 데이터에서 가격을 가져옵니다. 또한, 팩터 데이터를 가져옵니다.
In [ ]:
performance = pd.read_pickle('single_factor.pickle')
In [ ]:
performance.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 755 entries, 2015-01-02 21:00:00+00:00 to 2017-12-29 21:00:00+00:00 Data columns (total 39 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 period_open 755 non-null datetime64[ns, UTC] 1 period_close 755 non-null datetime64[ns, UTC] 2 short_value 755 non-null float64 3 long_exposure 755 non-null float64 4 pnl 755 non-null float64 5 short_exposure 755 non-null float64 6 capital_used 755 non-null float64 7 orders 755 non-null object 8 transactions 755 non-null object 9 gross_leverage 755 non-null float64 10 positions 755 non-null object 11 net_leverage 755 non-null float64 12 starting_exposure 755 non-null float64 13 returns 755 non-null float64 14 ending_exposure 755 non-null float64 15 starting_value 755 non-null float64 16 ending_value 755 non-null float64 17 starting_cash 755 non-null float64 18 ending_cash 755 non-null float64 19 portfolio_value 755 non-null float64 20 longs_count 755 non-null int64 21 shorts_count 755 non-null int64 22 long_value 755 non-null float64 23 algo_volatility 754 non-null float64 24 benchmark_period_return 755 non-null float64 25 benchmark_volatility 754 non-null float64 26 algorithm_period_return 755 non-null float64 27 alpha 0 non-null object 28 beta 0 non-null object 29 sharpe 753 non-null float64 30 sortino 753 non-null float64 31 max_drawdown 755 non-null float64 32 max_leverage 755 non-null float64 33 excess_return 755 non-null float64 34 treasury_period_return 755 non-null float64 35 trading_days 755 non-null int64 36 period_label 755 non-null object 37 factor_data 754 non-null object 38 prices 754 non-null object dtypes: datetime64[ns, UTC](2), float64(26), int64(3), object(8) memory usage: 235.9+ KB
In [ ]:
prices = pd.concat([df.to_frame(d) for d, df in performance.prices.dropna().items()],axis=1).T
prices.columns = [re.findall(r"\[(.+)\]", str(col))[0] for col in prices.columns]
prices.index = prices.index.normalize()
prices.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 754 entries, 2015-01-05 00:00:00+00:00 to 2017-12-29 00:00:00+00:00 Columns: 1649 entries, A to NETE dtypes: float64(1649) memory usage: 9.5 MB
In [ ]:
factor_data = pd.concat([df.to_frame(d) for d, df in performance.factor_data.dropna().items()],axis=1).T
factor_data.columns = [re.findall(r"\[(.+)\]", str(col))[0] for col in factor_data.columns]
factor_data.index = factor_data.index.normalize()
factor_data = factor_data.stack()
factor_data.index.names = ['date', 'asset']
factor_data.head()
Out[ ]:
date asset
2015-01-05 00:00:00+00:00 A 2707.0
AAL 870.0
AAP 1253.0
AAPL 2977.0
ABBV 2806.0
dtype: float64
In [ ]:
with pd.HDFStore('./assets.h5') as store:
sp500 = store['sp500/stooq'].close
sp500 = sp500.resample('D').ffill().tz_localize('utc').filter(prices.index.get_level_values(0))
sp500.head()
Out[ ]:
Date 2015-01-05 00:00:00+00:00 2020.58 2015-01-06 00:00:00+00:00 2002.61 2015-01-07 00:00:00+00:00 2025.90 2015-01-08 00:00:00+00:00 2062.14 2015-01-09 00:00:00+00:00 2044.81 Name: close, dtype: float64
get_clean_factor_and_forward_returns 유틸리티 함수를 사용하여 필요한 형식의 알파렌즈 입력 데이터를 생성할 수 있습니다. 이 함수는 신호 사분위수와 주어진 보유 기간에 대한 전방 수익률도 반환합니다.
In [ ]:
HOLDING_PERIODS = (5, 10, 21, 42)
QUANTILES = 5
alphalens_data = get_clean_factor_and_forward_returns(factor=factor_data,
prices=prices,
periods=HOLDING_PERIODS,
quantiles=QUANTILES)
Dropped 5.6% entries from factor data: 5.6% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions). max_loss is 35.0%, not exceeded: OK!
The alphalens_data DataFrame contains the returns on an investment in the given asset on a given date for the indicated holding period, as well as the factor value, that is, the asset's MeanReversion ranking on that date, and the corresponding quantile value:
In [ ]:
alphalens_data.head()
Out[ ]:
| 5D | 10D | 21D | 42D | factor | factor_quantile | ||
|---|---|---|---|---|---|---|---|
| date | asset | ||||||
| 2015-01-05 00:00:00+00:00 | A | 0.007789 | -0.046985 | -0.027889 | 0.072864 | 2707.0 | 5 |
| AAL | -0.079722 | -0.020882 | -0.095684 | -0.103295 | 870.0 | 1 | |
| AAP | 0.015722 | -0.024350 | -0.003196 | -0.010865 | 1253.0 | 2 | |
| AAPL | 0.028235 | 0.023247 | 0.116518 | 0.214965 | 2977.0 | 5 | |
| ABBV | 0.017169 | -0.018561 | -0.061098 | -0.064811 | 2806.0 | 5 |
In [ ]:
alphalens_data.reset_index().head().to_csv('factor_data.csv', index=False)
The forward returns and the signal quantiles are the basis for evaluating the predictive power of the signal. Typically, a factor should deliver markedly different returns for distinct quantiles, such as negative returns for the bottom quintile of the factor values and positive returns for the top quantile.
Summary Tear Sheet¶
In [ ]:
create_summary_tear_sheet(alphalens_data)
Quantiles Statistics
| min | max | mean | std | count | count % | |
|---|---|---|---|---|---|---|
| factor_quantile | ||||||
| 1 | 1.0 | 1011.0 | 303.057978 | 188.562449 | 142313 | 20.020145 |
| 2 | 352.0 | 1636.0 | 856.678631 | 234.705707 | 142117 | 19.992572 |
| 3 | 794.0 | 2153.0 | 1417.174265 | 259.942787 | 142019 | 19.978786 |
| 4 | 1273.0 | 2621.0 | 1979.178578 | 253.504821 | 142117 | 19.992572 |
| 5 | 1827.0 | 3050.0 | 2519.578804 | 227.348609 | 142283 | 20.015925 |
Returns Analysis
| 5D | 10D | 21D | 42D | |
|---|---|---|---|---|
| Ann. alpha | 0.046 | 0.036 | 0.009 | 0.001 |
| beta | 0.083 | 0.098 | 0.077 | 0.019 |
| Mean Period Wise Return Top Quantile (bps) | 11.724 | 9.110 | 3.948 | -0.376 |
| Mean Period Wise Return Bottom Quantile (bps) | -16.862 | -13.259 | -4.742 | -1.979 |
| Mean Period Wise Spread (bps) | 28.587 | 22.343 | 8.711 | 1.582 |
Information Analysis
| 5D | 10D | 21D | 42D | |
|---|---|---|---|---|
| IC Mean | 0.022 | 0.026 | 0.017 | 0.003 |
| IC Std. | 0.140 | 0.127 | 0.116 | 0.115 |
| Risk-Adjusted IC | 0.160 | 0.207 | 0.148 | 0.027 |
| t-stat(IC) | 4.261 | 5.529 | 3.953 | 0.729 |
| p-value(IC) | 0.000 | 0.000 | 0.000 | 0.466 |
| IC Skew | 0.372 | 0.266 | 0.115 | 0.113 |
| IC Kurtosis | 0.054 | -0.515 | -0.333 | -0.557 |
Turnover Analysis
| 5D | 10D | 21D | 42D | |
|---|---|---|---|---|
| Quantile 1 Mean Turnover | 0.411 | 0.590 | 0.830 | 0.831 |
| Quantile 2 Mean Turnover | 0.645 | 0.740 | 0.804 | 0.812 |
| Quantile 3 Mean Turnover | 0.679 | 0.765 | 0.808 | 0.812 |
| Quantile 4 Mean Turnover | 0.642 | 0.741 | 0.810 | 0.814 |
| Quantile 5 Mean Turnover | 0.394 | 0.569 | 0.811 | 0.819 |
| 5D | 10D | 21D | 42D | |
|---|---|---|---|---|
| Mean Factor Rank Autocorrelation | 0.713 | 0.454 | -0.013 | -0.017 |
<Figure size 640x480 with 0 Axes>
Predictive performance by factor quantiles - Returns Analysis¶
- 팩터 5분위수에 대한 예측 성과
첫 번째 단계로, 우리는 팩터 사분위별로 평균 기간 수익률을 시각화하고자 합니다. 성과 모듈의 mean_return_by_quantile 내장 함수와 플로팅팅 모듈의 plot_quantile_returns_bar 함수를 사용할 수 있습니다.
In [ ]:
mean_return_by_q, std_err = mean_return_by_quantile(alphalens_data)
mean_return_by_q_norm = mean_return_by_q.apply(lambda x: x.add(1).pow(1/int(x.name[:-1])).sub(1))
Mean Return by Holding Period and Quintile¶
결과는 팩터 신호의 사분위별로 다른 보유 기간에 대한 전방 수익률의 평균을 나타내는 막대형 차트입니다. 볼 수 있듯이, 가장 짧은 보유 기간을 제외하고는 하위 사분위는 상위 사분위에 비해 현저히 부정적인 결과를 나타내고 있습니다.
In [ ]:
plot_quantile_returns_bar(mean_return_by_q)
plt.tight_layout()
sns.despine();
10일 보유 기간은 첫 번째와 네 번째 사분위에 약간 더 좋은 결과를 제공합니다. 또한, 신호 사분위별로 주도된 투자의 성과를 시간에 따라 확인하고자 합니다.
5일 보유 기간에 대해 일별 수익률을 계산하고, 알파렌즈는 일별 신호와 더 긴 보유 기간 간의 불일치를 고려하여 기간 수익률을 조정할 것입니다 (자세한 내용은 문서를 참조하십시오).
In [ ]:
mean_return_by_q_daily, std_err = mean_return_by_quantile(alphalens_data, by_date=True)
Cumulative 5D Return¶
The resulting line plot shows that, for most of this three-year period, the top two quintiles significantly outperformed the bottom two quintiles. However, as suggested by the previous plot, signals by the fourth quintile produced a better performance than those by the top quintile
In [ ]:
plot_cumulative_returns_by_quantile(mean_return_by_q_daily['5D'], period='5D', freq=None)
plt.tight_layout()
sns.despine();
Return Distribution by Holding Period and Quintile¶
This distributional plot highlights that the range of daily returns is fairly wide and, despite different means, the separation of the distributions is very limited so that, on any given day, the differences in performance between the different quintiles may be rather limited:
In [ ]:
plot_quantile_returns_violin(mean_return_by_q_daily)
plt.tight_layout()
sns.despine();
Information Coefficient¶
- 정보 계수
이 책의 대부분은 ML 모델을 사용하여 알파 팩터를 설계하는 것에 관한 내용입니다. ML은 예측적 목표를 최적화하는 것이며, 이 섹션에서는 알파 팩터의 성능을 측정하는 데 사용되는 주요 지표를 소개하겠습니다.
알파를 기준 대비 평균 수익으로 정의하고, 이로 인해 정보비율(IR)이라는 지표가 생성됩니다. 정보비율은 알파를 추적 리스크로 나누어 평균 초과 수익을 위험 단위로 측정하는 지표입니다.
리스크-무위험 이자율이 기준인 경우, 정보비율은 잘 알려진 샤프 지수에 해당하며, 수익률이 정규 분포를 따르지 않는 일반적인 경우에 발생하는 중요한 통계 측정 문제를 강조할 것입니다. 또한, 액티브 관리의 기본 법칙에 대해 설명할 것인데, 이는 정보비율을 예측 기술의 조합과 전략이 예측 기술을 효과적으로 이용하는 능력으로 분해합니다.
5D Information Coefficient (Rolling Average)¶
알파 팩터의 목표는 미래 수익의 정확한 방향성 예측입니다. 따라서 알파 팩터의 예측과 대상 자산의 전방 수익과의 상관관계는 자연스러운 성능 측정 지표입니다.
선형 관계의 강도를 측정하는 피어슨 상관관계와는 달리, 두 변수 사이의 관계를 단조 함수로 얼마나 잘 설명할 수 있는지 측정하는 비모수적인 스피어만 순위 상관계수를 사용하는 것이 더 좋습니다.
알파렌즈를 사용하여 정보 계수를 얻을 수 있으며, 이는 내부적으로 scipy.stats.spearmanr을 사용합니다.
factor_information_coefficient 함수는 기간별 상관관계를 계산하고, plot_ic_ts는 월 이동 평균을 사용한 시계열 그래프를 생성합니다:
In [ ]:
ic = factor_information_coefficient(alphalens_data)
plot_ic_ts(ic[['5D']])
plt.tight_layout()
sns.despine();
Information Coefficient by Holding Period¶
이 시계열 그래프는 이동 평균 정보 계수(IC)가 상당히 양의 값을 가지는 연장된 기간을 보여줍니다. 정보 계수가 0.05 또는 0.1인 경우, 이러한 예측 기술을 적용할 충분한 기회가 있다면 상당한 성과를 낼 수 있습니다. 이는 액티브 관리의 기본 법칙이 설명하는 바입니다.
연간 평균 IC의 그래프를 살펴보면 해당 팩터의 성능이 역사적으로 불규칙하다는 것을 강조합니다.
In [ ]:
ic = factor_information_coefficient(alphalens_data)
ic_by_year = ic.resample('A').mean()
ic_by_year.index = ic_by_year.index.year
ic_by_year.plot.bar(figsize=(14, 6))
plt.tight_layout();
Turnover Tear Sheet¶
- 팩터 회전율
팩터 회전율은 특정 사분위수와 연결된 자산이 얼마나 자주 변경되는지, 즉 포트폴리오를 신호의 순서에 맞게 조정하기 위해 몇 번의 거래가 필요한지를 측정합니다. 구체적으로 말하면, 현재 팩터 사분위수에 있는 자산 중 이전 기간에는 해당 사분위수에 속하지 않았던 자산의 비율을 측정합니다.
- alphalens 버전 호환 오류
In [ ]:
create_turnover_tear_sheet(alphalens_data);
In [ ]:
알파 팩터 리소스¶
기타 알고리듬 트레이딩 라이브러리¶
- QuantConnect
- Alpha Trading Labs
- Alpha Trading Labs is no longer active
- WorldQuant
- Python Algorithmic Trading Library PyAlgoTrade
- pybacktest
- Trading with Python
- Interactive Brokers
In [ ]:
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