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Areix-IO-0.1.9
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مشاهده بیشترتوضیحات
Areix IO Backtesting Framework
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | Areix-IO-0.1.9 |
| نام | Areix-IO |
| نسخه کتابخانه | 0.1.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Areix |
| ایمیل نویسنده | hellohk@areix-ai.com |
| آدرس صفحه اصلی | https://alphagen.areix-ai.com |
| آدرس اینترنتی | https://pypi.org/project/Areix-IO/ |
| مجوز | - |
# Areix IO (Alpha Test)
[Documentation](http://alphagen.areix-ai.com/doc)
## Installation
Create a virtual environment
```
virtualenv venv --python=python3
```
Activate the virtual environment
```python
# 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:
```python
from areix_io import (
create_report_folder, Side,
Strategy, BinanceSpotDataFeed, BackTest,
BackTestBroker, CommissionScheme
)
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
# df = df.dropna().astype(float)
return df
def get_X(data):
return data.filter(like='x_').values
def get_y(data):
y = data.close.pct_change(PRED_DAYS).shift(-PRED_DAYS) # Returns after roughly two days
y[y.between(-PCT_CHANGE, PCT_CHANGE)] = 0 # Devalue returns smaller than 0.4%
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(Strategy):
num_pre_train = 300
def initialize(self):
"""
Model training step
"""
self.info('initialize')
self.code = list(self.ctx.feed.keys())[0]
df = self.ctx.feed[self.code].data
self.ctx.feed[self.code].data = update_df(df)
self.y = get_y(df[self.num_pre_train-1:])
self.y_true = self.y.values
self.clf = KNeighborsClassifier(7)
df = df.dropna()
X, y = get_clean_Xy(df[: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.my_quantity = self.ctx.get_quantity(order['code'])
self.info(
f"{order['side'].name} order [number {order['id']}] ({order['order_type'].name}) executed [quantity {order['quantity']}] {order['code']} [price ${order['price']:2f}] [Cost ${order['gross_amount']:2f}] [Commission: ${order['commission']}] [Available Cash: ${self.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]
X = get_X(bar_data)
if bar_data.x_ma100 != bar_data.x_ma100:
return
forecast = self.clf.predict([X])[0]
self.y_pred.append(forecast)
open, high, low, close = bar_data.open, bar_data.high, bar_data.low, bar_data.close
upper, lower = close * (1 + np.r_[1, -1]*PCT_CHANGE)
if forecast == 1 and not self.ctx.get_quantity(self.code):
o1 = self.order_amount(code=self.code,amount=self.available_cash,side=Side.BUY)
self.info(f"BUY order [number {o1['id']}] created, [quantity {o1['quantity']}] [price {o1['price']}] [balance: {self.available_cash}]")
osl = self.sell(code=self.code,quantity=o1['quantity'], price=lower, stop_price=lower)
self.info(f"STOPLOSS order [number {osl['id']}] created, [quantity {osl['quantity']}] [price {osl['price']}] [balance: {self.available_cash}]")
elif forecast == -1 and self.ctx.get_quantity(self.code):
o2 = self.close(code=self.code, price=upper)
self.info(f"SELL order [number {o2['id']}] created, [quantity {o2['quantity']}] [price {o2['price']}] [balance: {self.available_cash}]")
```
Run your strategy:
```python
if __name__ == '__main__':
base = create_report_folder()
start_date = '2021-01-01'
end_date = '2021-07-01'
interval = '4h'
code = 'XRP/USDT'
benchmark_code = 'BTC/USDT'
cs = CommissionScheme(
commission_rate=0.001,
min_commission=None,
is_contract = False
)
broker = BackTestBroker(
commission_scheme=cs,
trade_at='close',
# trade_at='open',
cash=1000,
short_cash=False,
slippage=0.0)
btc = BinanceSpotDataFeed(
symbol=benchmark_code,
start_date=start_date,
end_date=end_date,
interval=interval,
min_volume = 0.00001,
order_ascending=True,
store_path=base
)
datefeed = BinanceSpotDataFeed(
symbol=code,
start_date=start_date,
end_date=end_date,
interval=interval,
min_volume = 0.0001,
order_ascending=True,
store_path=base
)
mytest = BackTest(
[ datefeed ],
MLStrategy,
benchmark=btc,
store_path=base,
broker=broker
)
mytest.start()
```
Retrieve statistic results:
```python
prefix = ''
stats = mytest.ctx.statistic.stats(pprint=True, annualization=252, risk_free=0.0442)
stats['model_name'] = 'Simple KNN Signal Generation Strategy'
stats['algorithm'] = ['KNN', 'Simple Moving Average', 'Bollinger Band']
print(stats)
mytest.contest_output(is_plot=True)
```
Result:
```
start 2021-01-01 00:00:00+08:00
end 2021-07-01 00:00:00+08:00
duration 181 days 00:00:00
trading_pairs [XRPUSDT]
benchmark BTCUSDT
beginning_balance 1000
ending_balance 6374.787763
total_net_profit 5374.787763
gross_profit 9919.319576
gross_loss -4544.531813
profit_factor 2.182693
return_on_initial_capital 5.374788
annualized_return 0.536384
total_return 5.374788
max_return 5.401244
min_return -0.004806
number_trades 630
number_winning_trades 317
number_losing_trades 220
avg_daily_trades 6.899160
avg_weekly_trades 32.840000
avg_monthly_trades 136.833333
win_ratio 0.503175
loss_ratio 0.349206
win_days 64
loss_days 18
max_win_in_day 701.189561
max_loss_in_day -51.928662
max_consecutive_win_days 22
max_consecutive_loss_days 2
avg_profit_per_trade 13.209755
trading_period 0 years 6 months 0 days
avg_daily_pnl($) 4.949160
avg_daily_pnl 0.001784
avg_weekly_pnl($) 206.722606
avg_weekly_pnl 0.080719
avg_monthly_pnl($) 868.835518
avg_monthly_pnl 0.398852
avg_quarterly_pnl($) 1532.511026
avg_quarterly_pnl 0.463027
avg_annualy_pnl($) NaN
avg_annualy_pnl NaN
sharpe_ratio 2.022460
sortino_ratio 6.971285
annualized_volatility 0.200512
omega_ratio 0.011848
downside_risk 0.064485
information_ratio 0.058498
beta 0.105311
alpha -0.999939
calmar_ratio 5.527284
tail_ratio 9.548038
max_drawdown 0.097043
max_drawdown_period (2021-06-21 04:00:00+08:00, 2021-06-28 08:00:0...
max_drawdown_duration 7 days 04:00:00
sqn 5.283288
model_name Simple KNN Signal Generation Strategy
algorithm [KNN, Simple Moving Average, Bollinger Band]
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8.0 | Python |
نحوه نصب
نصب پکیج whl Areix-IO-0.1.9:
pip install Areix-IO-0.1.9.whl
نصب پکیج tar.gz Areix-IO-0.1.9:
pip install Areix-IO-0.1.9.tar.gz