Pandas TA 是一个基于Pandas模块开发的,具有上百个技术指标和常用指标的开源模块。它包括但不限于能够绘制62种蜡烛形态(晨星、乌云、十字星、孕线等等)、130个技术指标,如移动平均线、macd、hma、布林带、obv、aron、squeeze等等各种指标。
下面就来讲一下这个量化投资神器的安装和使用方法,如果对你有帮助,记得点个赞和在看支持一下哦。
1.准备
开始之前,你要确保Python和pip已经成功安装在电脑上,如果没有,请访问这篇文章:超详细Python安装指南 进行安装。
(可选1) 如果你用Python的目的是数据分析,可以直接安装Anaconda:Python数据分析与挖掘好帮手—Anaconda,它内置了Python和pip.
(可选2) 此外,推荐大家用VSCode编辑器来编写小型Python项目:Python 编程的最好搭档—VSCode 详细指南
Windows环境下打开Cmd(开始—运行—CMD),苹果系统环境下请打开Terminal(command+空格输入Terminal),输入命令安装依赖:
pip install pandas_ta
此外,如果你想使用上全部指标,你需要安装TA-Lib
pip install Ta-Lib
安装TA-Lib的时候可能会遇到没有VC++14.0的报错,这时候我们需要手动安装,在 https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib 中下载你对应的Python版本的 TA-Lib whl文件。
下载完成后执行以下命令:
# 公众号:二七阿尔量化 pip install D:\path\TA_lib‑0.4.24‑cp38‑cp38‑win_amd64.whl
就能成功手动安装 Ta-lib
2.Pandas TA 基本使用
为了方便介绍使用方法,我下载了沪深300的分钟级数据,如果你需要本文的全部代码及数据,请在二七阿尔量化公众号后台回复:pandas_ta 下载。
首先看看我们的数据类型:
# 公众号:二七阿尔量化 import pandas as pd import pandas_ta as ta sh300data = pd.read_csv("sh300_1min.csv") print(sh300data) # Unnamed: 0 day open high low close volume # 0 0 2022-03-07 10:47:00 4406.223 4406.352 4405.662 4405.922 54345400 # 1 1 2022-03-07 10:48:00 4406.172 4406.175 4403.834 4403.918 70803100 # 2 2 2022-03-07 10:49:00 4403.333 4403.333 4402.235 4402.340 49632500 # 3 3 2022-03-07 10:50:00 4402.330 4402.519 4401.838 4402.519 48159200
默认情况下,pandas ta 取 open 、high、low、close、volume、adj_close 列作为指标参数,本文的列名正好对应的上,无需再去特别命名。
先试试收益率计算:
sh300data = sh300data.set_index("day") print(sh300data.ta.log_return(cumulative=True, append=True)) # 2022-03-07 10:47:00 0.000000 # 2022-03-07 10:48:00 -0.000455 # 2022-03-07 10:49:00 -0.000813 # 2022-03-07 10:50:00 -0.000773 # 2022-03-07 10:51:00 -0.000826
验证一下结果:
>>> (- 4405.922 + 4403.918) / 4405.922 -0.00045484236897518966 >>> (- 4403.918 + 4402.34) / 4403.918 -0.00035831729836920665 >>> -0.00045484236897518966 + -0.00035831729836920665 -0.0008131596673443963
结果对得上,注意我们传递了cumulative=True参数,因此每次都会将前面的收益率累加。
试一下常用指标 sma:
sma10 = sh300data.ta.sma(length=10) print(sma10) # day # 2022-03-07 10:47:00 NaN # 2022-03-07 10:48:00 NaN # 2022-03-07 10:49:00 NaN # 2022-03-07 10:50:00 NaN # 2022-03-07 10:51:00 NaN # ... # 2022-07-08 14:55:00 4429.3936 # 2022-07-08 14:56:00 4428.9421 # 2022-07-08 14:57:00 4428.5996 # 2022-07-08 14:58:00 4428.3280 # 2022-07-08 15:00:00 4428.1877 # Name: SMA_10, Length: 20000, dtype: float64
支持以下全部技术指标:aberration, above, above_value, accbands, ad, adosc, adx, alma, amat, ao, aobv, apo, aroon, atr, bbands, below, below_value, bias, bop, brar, cci, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, coppock, cross, cross_value, cti, decay, decreasing, dema, dm, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, increasing, inertia, jma, kama, kc, kdj, kst, kurtosis, kvo, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, squeeze, squeeze_pro, ssf, stc, stdev, stoch, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, tos_stdevall, trima, trix, true_range, tsi, tsignals, ttm_trend, ui, uo, variance, vhf, vidya, vortex, vp, vwap, vwma, wcp, willr, wma, xsignals, zscore
试一下十字星(Doji Candlestick)形态:
doji = sh300data.ta.cdl_pattern(name="doji") print(doji) # CDL_DOJI_10_0.1 # day # 2022-03-07 10:47:00 0.0 # 2022-03-07 10:48:00 0.0 # 2022-03-07 10:49:00 0.0 # 2022-03-07 10:50:00 0.0 # 2022-03-07 10:51:00 0.0 # ... ... # 2022-07-08 14:55:00 0.0 # 2022-07-08 14:56:00 100.0 # 2022-07-08 14:57:00 0.0 # 2022-07-08 14:58:00 0.0 # 2022-07-08 15:00:00 0.0
这里的值如果出现 100 ,就是出现DOJI形态。支持以下全部蜡烛形态:
2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers, abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack, darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar, gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave, hikkake, hikkakemod, homingpigeon, identical3crows, inneck, inside, invertedhammer, kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow, mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods, separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich, takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods
由于62种蜡烛形态太多了,你可能需要一次性捞出来,Pandas TA也支持你这么做:
# 公众号:二七阿尔量化 import pandas as pd import pandas_ta as ta sh300data = pd.read_csv("sh300_1min.csv") sh300data = sh300data.set_index("day") all_candle = sh300data.ta.cdl_pattern(name="all") print(all_candle) # CDL_2CROWS CDL_3BLACKCROWS CDL_3INSIDE ... CDL_UNIQUE3RIVER CDL_UPSIDEGAP2CROWS CDL_XSIDEGAP3METHODS # day ... # 2022-03-07 10:47:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-03-07 10:48:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-03-07 10:49:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-03-07 10:50:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-03-07 10:51:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # ... ... ... ... ... ... ... ... # 2022-07-08 14:55:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-07-08 14:56:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-07-08 14:57:00 0.0 0.0 100.0 ... 0.0 0.0 0.0 # 2022-07-08 14:58:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # 2022-07-08 15:00:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 # [20000 rows x 62 columns]
3.高级使用
Pandas TA 还支持策略的使用:
# 公众号:二七阿尔量化 import pandas as pd import pandas_ta as ta from multiprocessing import Process, freeze_support sh300data = pd.read_csv("sh300_1min.csv") sh300data = sh300data.set_index("day") CustomStrategy = ta.Strategy( name="Momo and Volatility", description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20", ta=[ {"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}, {"kind": "bbands", "length": 20}, {"kind": "rsi"}, {"kind": "macd", "fast": 8, "slow": 21}, {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"}, ] ) if __name__ == '__main__': freeze_support() sh300data.ta.strategy(CustomStrategy) print(sh300data) # Unnamed: 0 open high low ... MACD_8_21_9 MACDh_8_21_9 MACDs_8_21_9 VOLUME_SMA_20 # day ... # 2022-03-07 10:47:00 0 4406.223 4406.352 4405.662 ... NaN NaN NaN NaN # 2022-03-07 10:48:00 1 4406.172 4406.175 4403.834 ... NaN NaN NaN NaN # 2022-03-07 10:49:00 2 4403.333 4403.333 4402.235 ... NaN NaN NaN NaN # 2022-03-07 10:50:00 3 4402.330 4402.519 4401.838 ... NaN NaN NaN NaN # 2022-03-07 10:51:00 4 4402.376 4402.699 4402.129 ... NaN NaN NaN NaN # ... ... ... ... ... ... ... ... ... ... # 2022-07-08 14:55:00 19995 4428.123 4428.371 4427.098 ... -1.700179 -0.244194 -1.455985 50578250.0 # 2022-07-08 14:56:00 19996 4427.209 4427.688 4426.886 ... -1.725356 -0.215496 -1.509860 53128625.0 # 2022-07-08 14:57:00 19997 4427.279 4428.605 4427.279 ... -1.583555 -0.058956 -1.524599 55393515.0 # 2022-07-08 14:58:00 19998 4428.268 4428.458 4428.268 ... -1.426088 0.078808 -1.504897 53840375.0 # 2022-07-08 15:00:00 19999 4427.963 4428.781 4427.963 ... -1.241029 0.211094 -1.452123 60235755.0 # [20000 rows x 18 columns]
可以看到,策略其实就是让你将一些技术指标提前配置好,通过调用策略能够自动将这些技术指标附加到你的数据集上,非常方便。
此外,策略计算的时候会用到多进程,多进程的并行数量也是可以控制的:
# 设置为4个核心,即最多4个并行 sh300data.ta.cores = 4 # 设置为0则不用多进程 sh300data.ta.cores = 0 # 查看并行数量 print(sh300data.ta.cores) # 0
好啦,关于Pandas TA我们就先介绍到这里啦,如果你需要了解更多内容,可以访问官方文档:
https://github.com/twopirllc/pandas-ta
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