Areix IO Backtesting Framework
Project description
Areix IO (Alpha Test)
Installation
Create a virtual environment
virtualenv venv --python=python3
Activate the virtual environment
# Macbook / Linus
source venv/bin/activate
# Windows
venv/Scripts/activate
Deactivate
deactivate
Install Areix-IO package
pip install Areix-IO
Usage
Define your strategy class:
import areix_io as aio
from areix_io.utils import create_report_folder, SideType
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
import pandas as pd
import numpy as np
PRED_DAYS = 2
PCT_CHANGE = 0.004
'''
Data pre processing step
'''
def bollinger_band(data, n_lookback, n_std):
hlc3 = (data['high'] + data['low'] + data['close']) / 3
mean, std = hlc3.rolling(n_lookback).mean(), hlc3.rolling(n_lookback).std()
upper = mean + n_std*std
lower = mean - n_std*std
return upper, lower
def update_df(df):
upper, lower = bollinger_band(df, 20, 2)
df['ma10'] = df.close.rolling(10).mean()
df['ma20'] = df.close.rolling(20).mean()
df['ma50'] = df.close.rolling(50).mean()
df['ma100'] = df.close.rolling(100).mean()
df['x_ma10'] = (df.close - df.ma10) / df.close
df['x_ma20'] = (df.close - df.ma20) / df.close
df['x_ma50'] = (df.close - df.ma50) / df.close
df['x_ma100'] = (df.close - df.ma100) / df.close
df['x_delta_10'] = (df.ma10 - df.ma20) / df.close
df['x_delta_20'] = (df.ma20 - df.ma50) / df.close
df['x_delta_50'] = (df.ma50 - df.ma100) / df.close
df['x_mom'] = df.close.pct_change(periods=2)
df['x_bb_upper'] = (upper - df.close) / df.close
df['x_bb_lower'] = (lower - df.close) / df.close
df['x_bb_width'] = (upper - lower) / df.close
return df
def get_X(data):
return data.filter(like='x').values
def get_y(data):
# use price change in the future 2 days as training label
y = data.close.pct_change(PRED_DAYS).shift(-PRED_DAYS)
y[y.between(-PCT_CHANGE, PCT_CHANGE)] = 0
y[y > 0] = 1
y[y < 0] = -1
return y
def get_clean_Xy(df):
X = get_X(df)
y = get_y(df).values
isnan = np.isnan(y)
X = X[~isnan]
y = y[~isnan]
return X, y
class MLStrategy(aio.Strategy):
num_pre_train = 300
def initialize(self):
'''
Model training step
'''
self.info('initialize')
self.code = 'XRP/USDT'
df = self.ctx.feed[self.code]
self.ctx.feed[self.code] = update_df(df)
self.y = get_y(df[self.num_pre_train-1:])
self.y_true = self.y.values
self.clf = KNeighborsClassifier(7)
tmp = df.dropna().astype(float)
X, y = get_clean_Xy(tmp[:self.num_pre_train])
self.clf.fit(X, y)
self.y_pred = []
def before_trade(self, order):
return True
def on_order_ok(self, order):
self.info(f"{order['side'].name} order {order['id']} ({order['order_type'].name}) executed #{order['quantity']} {order['code']} @ ${order['price']:2f}; Commission: ${order['commission']}; Available Cash: ${self.ctx.available_cash}; Position: #{self.ctx.get_quantity(order['code'])}; Gross P&L : ${order['pnl']}; Net P&L : ${order['pnl_net']}")
if not order['is_open']:
self.info(f"Trade closed, pnl: {order['pnl']}========")
def on_market_start(self):
# self.info('on_market_start')
pass
def on_market_close(self):
# self.info('on_market_close')
pass
def on_order_timeout(self, order):
self.info(f'on_order_timeout. Order: {order}')
pass
def finish(self):
self.info('finish')
def on_bar(self, tick):
'''
Model scoring and decisioning step
'''
bar_data = self.ctx.bar_data[self.code]
hist_data = self.ctx.hist_data[self.code]
if len(hist_data) < self.num_pre_train:
return
open, high, low, close = bar_data.open, bar_data.high, bar_data.low, bar_data.close
X = get_X(bar_data)
forecast = self.clf.predict([X])[0]
self.y_pred.append(forecast)
self.ctx.cplot(forecast,'Forcast')
self.ctx.cplot(self.y[tick],'Groundtruth')
# self.info(f"focasing result: {forecast}")
upper, lower = close * (1 + np.r_[1, -1]*PCT_CHANGE)
if forecast == 1 and not self.ctx.get_position(self.code):
o1 = self.order_amount(code=self.code,amount=200000,side=SideType.BUY, asset_type='Crypto')
self.info(f"BUY order {o1['id']} created #{o1['quantity']} @ {close:2f}")
osl = self.sell(code=self.code,quantity=o1['quantity'], price=lower, stop_price=lower, asset_type='Crypto')
self.info(f"STOPLOSS order {osl['id']} created #{osl['quantity']} @ {lower:2f}")
elif forecast == -1 and self.ctx.get_position(self.code):
o2 = self.order_amount(code=self.code,amount=200000,side=SideType.SELL, price=upper, asset_type='Crypto',ioc=True)
self.info(f"SELL order {o2['id']} created #{o2['quantity']} @ {close:2f}")
Run your strategy:
if __name__ == '__main__':
aio.set_token('xxxxxx') # Only need to run once
base = create_report_folder()
start_date = '2020-12-01'
end_date = '2021-03-12'
sdf = aio.CryptoDataFeed(
symbols=['XRP/USDT', 'BTC/USDT'],
start_date=start_date,
end_date=end_date,
interval='4h',
order_ascending=True,
store_path=base
)
feed, idx = sdf.fetch_data()
benchmark = feed.pop('BTC/USDT')
mytest = aio.BackTest(
feed,
MLStrategy,
commission_rate=0.001,
min_commission=0,
trade_at='close',
benchmark=benchmark,
cash=200000,
tradedays=idx,
store_path=base
)
mytest.start()
Retrieve statistic results:
prefix = ''
stats = mytest.ctx.statistic.stats(pprint=True, annualization=252, risk_free=0.0442)
'''
Model evaluation step
'''
stats['model_name'] = 'Simple KNN Signal Generation Strategy'
stats['algorithm'] = ['KNN', 'Simple Moving Average', 'Bollinger Band']
stats['model_measures'] = ['f1-score','accuracy']
ytrue = mytest.ctx.strategy.y_true[:-PRED_DAYS]
ypred = mytest.ctx.strategy.y_pred[:-PRED_DAYS]
# print(len(ytrue),len(ypred), ytrue, ypred)
stats['f1-score'] = f1_score(ytrue, ypred,average='weighted')
stats['accuracy'] = accuracy_score(ytrue, ypred)
print(stats)
mytest.contest_output()
Result:
start 2020-12-01 00:00:00+08:00
end 2021-03-12 00:00:00+08:00
duration 101 days 00:00:00
beginning_balance 200000
ending_balance 401903.976915
total_net_profit 201903.976915
gross_profit 307614.927695
gross_loss -105710.950780
profit_factor 2.909963
return_on_initial_capital 1.009520
annualized_return 0.336075
total_return 1.009520
max_return 1.009520
min_return 0.000000
number_trades 185
number_winning_trades 53
number_losing_trades 42
avg_daily_trades 5.606061
avg_weekly_trades 26.428571
avg_monthly_trades 61.666667
win_ratio 0.286486
loss_ratio 0.227027
win_days 30
loss_days 9
max_win_in_day 22655.999775
max_loss_in_day -2397.489768
max_consecutive_win_days 55
max_consecutive_loss_days 3
avg_profit_per_trade 2989.724497
trading_period 0 years 3 months 11 days
avg_daily_pnl($) 333.174879
avg_daily_pnl 0.001176
avg_weekly_pnl($) 14421.712637
avg_weekly_pnl 0.053603
avg_monthly_pnl($) 67301.325638
avg_monthly_pnl 0.274108
avg_quarterly_pnl($) 201903.976915
avg_quarterly_pnl 1.009520
avg_annualy_pnl($) 201903.976915
avg_annualy_pnl 1.009520
sharpe_ratio 2.270222
sortino_ratio 7.753067
annualized_volatility 0.111201
omega_ratio 0.000888
downside_risk 0.038236
information_ratio -0.036196
beta -0.021666
alpha -0.999988
calmar_ratio 10.806092
tail_ratio 3.428435
stability_of_timeseries 0.823200
max_drawdown 0.031100
max_drawdown_period (2021-02-13 00:00:00+08:00, 2021-02-13 16:00:0...
max_drawdown_duration 0 days 16:00:00
sqn 4.722520
model_name Simple KNN Signal Generation Strategy
algorithm [KNN, Simple Moving Average, Bollinger Band]
model_measures [f1-score, accuracy]
f1-score 0.468963
accuracy 0.493464
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