Onepy is an event-driven algorithmic trading Python library.
更新日志：Change Log
Onepy is developed using Python 3.x. You can install by pip and make sure they are up-to-date：
pip install pandas
pip install TA-Lib
pip install plotly
pip install funcy
pip install arrow
pip install pymongo
pip install OnePy_trader
pip install --upgrade OnePy_trader
OnePy安装完成后复制以下代码运行即可，可以迅速了解本框架的主要功能。 记得下载好data文件夹中的文件，设置好数据读取路径。 以Forex为例：
import OnePy as op
class MyStrategy(op.StrategyBase):
def __init__(self, marketevent):
super(MyStrategy, self).__init__(marketevent)
def prenext(self):
"""以下条件均可用于next中进行策略逻辑判断"""
# print(self.position[-1])
# print(self.margin[-1])
# print(self.avg_price[-1])
# print(self.unrealizedPL[-1])
# print(self.realizedPL[-1])
# print(self.commission[-1])
# print(self.cash[-1])
# print(self.balance[-1])
pass
def next(self):
"""这里写主要的策略思路"""
if self.i.SMA(period=30, index=-1) > self.i.SMA(period=50, index=-1):
if self.unrealizedPL[-1] <= 0:
self.buy(0.1, takeprofit=self.pips(200), # 设置止盈为200个pips，不可为负
stoploss=self.pct(1), # 设置止损为成交价的1%，不可为负
trailingstop=self.pips(60)) # 设置追踪止损，盈利时触发
else:
self.sell(0.05, price=self.pips(50), # 设置挂单，默认为第二天open价格加50点，也可为负数
takeprofit=self.pips(200),
stoploss=self.pips(200),
trailingstop=self.pips(60))
if self.unrealizedPL[-2] > self.unrealizedPL[-1] and self.unrealizedPL[-2] > 100:
self.exitall() # 设置浮亏浮盈大于100元且出现下降时清仓
go = op.OnePiece()
Forex = op.ForexCSVFeed(datapath='../data/EUR_USD30m.csv', instrument='EUR_USD',
fromdate='2012-04-01', todate='2012-05-01')
# 注意若要用MongoDB_Backtest_Feed，先运行tests里面的csv_to_MongoDB.py，推荐用MongoDB
# Forex = op.MongoDB_Backtest_Feed(database='EUR_USD', collection='M30', instrument='EUR_USD',
# fromdate='2012-04-01', todate='2012-05-01')
data_list = [Forex]
portfolio = op.Portfolio
strategy = MyStrategy
go.set_backtest(data_list, [strategy], portfolio, 'Forex')
go.set_commission(commission=10, margin=325, mult=100000)
go.set_cash(100000) # 设置初始资金
# go.set_pricetype(‘close’) # 设置成交价格为close，若不设置，默认为open
# go.set_notify() # 打印交易日志
go.sunny() # 开始启动策略
print(go.get_tlog('EUR_USD')) # 打印交易日志
go.get_analysis('EUR_USD')
go.plot(instrument='EUR_USD', notebook=False)结果：
+------------------------+
| Final_Value | 92619.2 |
| Total_return | -7.381% |
| Max_Drawdown | 9.261% |
| Duration | 989.0 |
| Sharpe_Ratio | -0.474 |
+------------------------+
+------------------------------------------------------------------+
| start | 2012-04-01 22:00:00 |
| end | 2012-04-30 23:30:00 |
| beginning_balance | 100000 |
| ending_balance | 92619.2 |
| unrealized_profit | -10032.85 |
| total_net_profit | 2652.05 |
| gross_profit | 2774.75 |
| gross_loss | -7.7 |
| profit_factor | 360.357 |
| return_on_initial_capital | 2.652 |
| annual_return_rate | -61.848 |
| trading_period | 0 years 0 months 29 days |
| pct_time_in_market | 469.595 |
| total_num_trades | 69 |
| num_winning_trades | 64 |
| num_losing_trades | 5 |
| num_even_trades | 0 |
| pct_profitable_trades | 92.754 |
| avg_profit_per_trade | 38.436 |
| avg_profit_per_winning_trade | 43.355 |
| avg_loss_per_losing_trade | -1.54 |
| ratio_avg_profit_win_loss | 28.153 |
| largest_profit_winning_trade | 80.5 |
| largest_loss_losing_trade | -2.5 |
| num_winning_points | 0.119 |
| num_losing_points | -0.326 |
| total_net_points | -0.207 |
| avg_points | -0.003 |
| largest_points_winning_trade | 0.007 |
| largest_points_losing_trade | -0.016 |
| avg_pct_gain_per_trade | -0.227 |
| largest_pct_winning_trade | 0.533 |
| largest_pct_losing_trade | -1.219 |
| max_consecutive_winning_trades | 54 |
| max_consecutive_losing_trades | 2 |
| avg_bars_winning_trades | 70.672 |
| avg_bars_losing_trades | 46.8 |
| max_closed_out_drawdown | -9.259 |
| max_closed_out_drawdown_start_date | 2012-04-02 09:00:00 |
| max_closed_out_drawdown_end_date | 2012-04-19 13:00:00 |
| max_closed_out_drawdown_recovery_date | Not Recovered Yet |
| drawdown_recovery | -0.047 |
| drawdown_annualized_return | 0.15 |
| max_intra_day_drawdown | -9.544 |
| avg_yearly_closed_out_drawdown | -4.166 |
| max_yearly_closed_out_drawdown | -6.346 |
| avg_monthly_closed_out_drawdown | -0.814 |
| max_monthly_closed_out_drawdown | -3.743 |
| avg_weekly_closed_out_drawdown | -0.273 |
| max_weekly_closed_out_drawdown | -2.997 |
| avg_yearly_closed_out_runup | 2.997 |
| max_yearly_closed_out_runup | 5.455 |
| avg_monthly_closed_out_runup | 0.673 |
| max_monthly_closed_out_runup | 4.458 |
| avg_weekly_closed_out_runup | 0.239 |
| max_weekly_closed_out_runup | 2.79 |
| pct_profitable_years | 28.816 |
| best_year | 2.843 |
| worst_year | -6.277 |
| avg_year | -1.436 |
| annual_std | 2.093 |
| pct_profitable_months | 42.54 |
| best_month | 4.458 |
| worst_month | -3.727 |
| avg_month | -0.147 |
| monthly_std | 1.17 |
| pct_profitable_weeks | 40.814 |
| best_week | 2.79 |
| worst_week | -2.997 |
| avg_week | -0.037 |
| weekly_std | 0.535 |
| sharpe_ratio | -0.474 |
| sortino_ratio | -0.466 |
+------------------------------------------------------------------+
- 事件驱动回测设计 ✓
- Forex模式 ✓
- Futures模式 ✓
- Stock模式 ✓
- 多品种回测(同一模式下) ✓
- 多策略回测 ✓
- 设置手续费，保证金/手，杠杆大小 ✓
- 设置成交价格为close或者第二天open ✓
- 设置是否打印交易日志 ✓
- Plot 画图模块 ✓
- Optimizer 参数优化模块
- To_MongoDB：自定义数据统一格式后存入数据库 ✓
- To_MongoDB：tickstory外汇数据CSV存入数据库 ✓
- To_MongoDB：tushare股票数据CSV存入数据库 ✓
- 直接tushare的api数据存入MongoDB ✓
- 自定义CSV数据读取 ✓
- tickstory外汇数据CSV读取 ✓
- Tushare股票数据CSV读取 ✓
- 期货数据CSV读取 ✓
- 从MongoDB数据库读取数据 ✓
- 实现做多Buy，做空Sell指令，一键平仓指令 ✓
- 按百分比pct或基点pips，挂多单(above&below)和挂空单(above&below) ✓
- 按百分比pct或基点pips，止盈止损 ✓
- 按百分比pct或基点pips，移动止损 ✓
- 自定义打印交易信息 ✓
- 技术指标Indicator模块 ✓
- OCO指令
- 挂单到时过期
- 取消挂单指令
- 暂无
- 模拟发送指令 ✓
- 模拟检查指令是否发送成功 ✓
- 打印交易日志 notify ✓
- 手续费commission，百分比类型和固定类型 ✓
- 计算保证金，仓位，总利润，总额，剩余现金，收益率，全部序列化 ✓
- 输出交易记录 ✓
- 交易结果超简单分析 ✓
- 交易记录详细分析 ✓
- 结合Benchmark分析
- vnpy
- Backtrader
- PyAlgoTrade
- Zipline
- Ultra-Finance
- ProfitPy
- pybacktest
- prophet
- quant
- AlephNull
- Trading with Python
- visualize-wealth
- tia: Toolkit for integration and analysis
- QuantSoftware Toolkit
- Pinkfish
- bt
- PyThalesians
- QSTrader
- QSForex
- pysystemtrade
- QTPyLib
- RQalpha
这个回测框架内部还存在很多问题，主要做学习之用，若想直接拿去回测思路还请三思。
如果你有什么想法欢迎随时和我交流。
感恩。
I'm very interested in your experience with Onepy.Please feel free to contact me via chenjiayicjy@gmail.com
Chandler_Chan