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Portfolio Backtesting

examples/cta_backtesting/portfolio_backtesting.ipynb

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python
from datetime import datetime

from vnpy_ctastrategy.backtesting import BacktestingEngine
from vnpy_ctastrategy.strategies.atr_rsi_strategy import AtrRsiStrategy
from vnpy_ctastrategy.strategies.boll_channel_strategy import BollChannelStrategy
python
def run_backtesting(strategy_class, setting, vt_symbol, interval, start, end, rate, slippage, size, pricetick, capital):
    engine = BacktestingEngine()
    engine.set_parameters(
        vt_symbol=vt_symbol,
        interval=interval,
        start=start,
        end=end,
        rate=rate,
        slippage=slippage,
        size=size,
        pricetick=pricetick,
        capital=capital
    )
    engine.add_strategy(strategy_class, setting)
    engine.load_data()
    engine.run_backtesting()
    df = engine.calculate_result()
    return df

def show_portafolio(df):
    engine = BacktestingEngine()
    engine.calculate_statistics(df)
    engine.show_chart(df)
python
df1 = run_backtesting(
    strategy_class=AtrRsiStrategy,
    setting={},
    vt_symbol="IF88.CFFEX",
    interval="1m",
    start=datetime(2019, 1, 1),
    end=datetime(2019, 4, 30),
    rate=0.3/10000,
    slippage=0.2,
    size=300,
    pricetick=0.2,
    capital=1_000_000,
    )
python
df2 = run_backtesting(
    strategy_class=BollChannelStrategy,
    setting={'fixed_size': 16},
    vt_symbol="RB88.SHFE",
    interval="1m",
    start=datetime(2019, 1, 1),
    end=datetime(2019, 4, 30),
    rate=1/10000,
    slippage=1,
    size=10,
    pricetick=1,
    capital=1_000_000,
    )
python
dfp = df1 + df2
dfp =dfp.dropna()
show_portafolio(dfp)