有人对数字货币很感兴趣,以下backtrader代码从币安交易所在线api获取1分钟k线,执行简单的双均线策略回测,我测试了,可直接运行,不过要翻墙才行。大家体验一下数字货币回测吧。
注意api有每次获取1000根k线的限制,所以程序里会分批获取。读到的数据放进dataframe,然后送入backtrader。
另外注意得到数据后,对数据类型的设置,特别是对日期时间索引字段的设置,设对了才能被backtrader接受。
。代码来自这里
# testBinance.py
# 从Binance币安在线api下载1分钟k线,进行回测
import requests
import backtrader as bt
import backtrader.analyzers as btanalyzers
import json
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
def get_binance_bars(symbol, interval, startTime, endTime):
url = "https://api.binance.com/api/v3/klines"
startTime = str(int(startTime.timestamp() * 1000))
endTime = str(int(endTime.timestamp() * 1000))
limit = '1000'
req_params = {"symbol" : symbol, 'interval' : interval, 'startTime' : startTime, 'endTime' : endTime, 'limit' : limit}
df = pd.DataFrame(json.loads(requests.get(url, params = req_params).text))
if (len(df.index) == 0):
return None
df = df.iloc[:, 0:6]
df.columns = ['datetime', 'open', 'high', 'low', 'close', 'volume']
df.open = df.open.astype("float")
df.high = df.high.astype("float")
df.low = df.low.astype("float")
df.close = df.close.astype("float")
df.volume = df.volume.astype("float")
df['adj_close'] = df['close']
df.index = [dt.datetime.fromtimestamp(x / 1000.0) for x in df.datetime]
return df
df_list = []
# 数据起点时间
last_datetime = dt.datetime(2020, 11, 23)
while True:
new_df = get_binance_bars('ETHUSDT', '1m', last_datetime, dt.datetime.now()) # 获取1分钟k线数据
if new_df is None:
break
df_list.append(new_df)
last_datetime = max(new_df.index) + dt.timedelta(0, 1)
df = pd.concat(df_list)
df.shape
class MaCrossStrategy(bt.Strategy):
def __init__(self):
ma_fast = bt.ind.SMA(period = 10)
ma_slow = bt.ind.SMA(period = 50)
self.crossover = bt.ind.CrossOver(ma_fast, ma_slow)
def next(self):
if not self.position:
if self.crossover > 0:
self.buy()
elif self.crossover < 0:
self.close()
cerebro = bt.Cerebro()
print('k线数量', len(df))
data = bt.feeds.PandasData(dataname = df)
cerebro.adddata(data)
cerebro.addstrategy(MaCrossStrategy)
cerebro.broker.setcash(1000000.0)
cerebro.addsizer(bt.sizers.PercentSizer, percents = 50)
cerebro.addanalyzer(btanalyzers.SharpeRatio, timeframe=bt.TimeFrame.Minutes, _name = "sharpe")
cerebro.addanalyzer(btanalyzers.Transactions, _name = "trans")
back = cerebro.run()
print('最终市值', cerebro.broker.getvalue()) # Ending balance
print(back[0].analyzers.sharpe.get_analysis()) # Sharpe
print(len(back[0].analyzers.trans.get_analysis())) # Number of Trades
编辑于 13 小时前