1.市场指标计算

1.1 MA指标

import talib
import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt
#from matplotlib.finance import candlestick2_ohlc
from mpl_finance import candlestick2_ohlc
from mpl_finance import candlestick_ohlc
#pip install https://github.com/matplotlib/mpl_finance/archive/master.zip

df = pd.read_csv('./000001.SZ.csv')
ma5_df=talib.MA(df['close'],timeperiod=5)
#print(ma_df)
ma10_df=talib.MA(df['close'],timeperiod=10)
ma20_df=talib.MA(df['close'],timeperiod=20)

fig=plt.figure()
ax=fig.add_subplot(111)
ax.plot(ma5_df,label='MA5')
ax.plot(ma10_df,label='MA10')
ax.plot(ma20_df,label='MA20')
candlestick2_ohlc(ax,df['open'],df['high'],df['low'],df['close'],width=0.6,colorup='red',colordown='green')
plt.legend()
plt.show()

1.2 MACD指标

diff ,dea,macd=talib.MACD(df['close'],fastperiod=12,slowperiod=26,signalperiod=9)

1.3 RSI指标

rsi=tailb.RSI(df['close'],timeperiod=14)

1.4 KDJ指标

K,D=talib.STOCH(df['high'],df['low'],df['close'],fastk_period=9,slowk_period=3,slowd_period=3)

1.5 CCI指标

CCI=talib.CCI(df['high'],df['low'],df['close'],timeperiod=14)

1.6 ATR指标

ATR=talib.ATR(df['high'],df['low'],df['close'],timeperiod=14)

1.7 OBV指标

OBV=talib.OBV(df['close'],df['vol'])

2 K线组合模式识别

2.1 晨星

nums=talib.CDLMORNINGSTAR(df['open'],df['high'],df['low'],df['close'])

2.2 昏星

nums=talib.CDLEVENINGSTAR(df['open'],df['high'],df['low'],df['close'])

2.3 锤子线

nums=talib.CDLHAMMER(df['open'],df['high'],df['low'],df['close'])

2.4 上吊线

nums=talib.CDLHANGINGMAN(df['open'],df['high'],df['low'],df['close'])

2.5 捉腰带线

nums=talib.CDLBELTHOLD(df['open'],df['high'],df['low'],df['close'])

3 FFn库

指标计算

import fnn
import pandas as pd
series=pd.Series([10000,10100,10300,10600])
#计算收益率
result=ffn.calc_total_return(series)
#计算年化收益率
ann_result=ffn.annualize(result,4,one_year=250)
#计算资产简单收益率
returns=ffn.to_return(series)
#计算夏普比率
sharpe=ffn.calc_sharpe(returns)
#计算最大回撤率
max_drawdown=ffn.calc_max_drowdown(series)
#计算所提诺比率
sortino=ffn.calc_sortino_ratio(returns)

4 两种经典策略

双均线策略

import tushare as ts

ts_pro=ts.pro_api('xxx')
df=ts_pro.daily(ts_code='000001.SZ',start_date='20180101',end_date='20191230')
df=df.reindex(index=df.index[::-1])
used_cols=['trade_date','close']
df=df[used_cols]
df.to_csv('./000001.SZ.csv',index=None)

#策略编写
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import ffn

hold=False
pos=0
capital=100000
rest=0
fee=0.0003
capital_list=[]
MA20_array=np.zeros(20)
MA10_array=np.zeros(10)
df=pd.read_csv('./000001.SZ.csv')

#数据遍历
for i in range(len(df)):
    price=df.loc[i,'close']
    date=df.loc[i,'trade_date']
    MA10_array[0:9]=MA10_array[1:10]
    MA20_array[0:91] = MA20_array[1:20]
    MA10_array[-1] = price
    MA20_array[-1] = price
    if i<20:
        continuce
    MA10 = MA10_array.mean()
    MA20 = MA20_array.mean()
#判断是否达到开仓和平仓的信号
if MA10>MA20 and hold==False:
    pos=int(capital/price/100)*100
    rest=capital-pos*price*(1+fee)
    hold=Ture
    print('buy at',date,'price',price,'capital',capital)
elif MA10<MA20 and hold==Ture:
    capital=pos*price*(1-fee)+rest
    pos=0
    hold=False
    print('sell at',date,'price',price,'capital',capital)

if hold==True:
    capital_list.append(rest+pos*price)
else:
    capital_list.append(capital)

capital_series=pd.Series(capital_list)
capital_returns=ffn.to_returns(capital_series)

RSI策略

 RSI6 = talib.RSI('rsi6_arry',timeperiod=6)[-1]