import pandas as pd


def SMA(array, n):
    """Simple moving average"""
    return pd.Series(array).rolling(n).mean()


def RSI(array, n):
    """Relative strength index"""
    # Approximate; good enough
    gain = pd.Series(array).diff()
    loss = gain.copy()
    gain[gain < 0] = 0
    loss[loss > 0] = 0
    rs = gain.ewm(n).mean() / loss.abs().ewm(n).mean()
    return 100 - 100 / (1 + rs)

from backtesting import Strategy, Backtest
from backtesting.lib import resample_apply


class System(Strategy):
    d_rsi = 30  # Daily RSI lookback periods
    w_rsi = 30  # Weekly
    level = 70
    
    def init(self):
        # Compute moving averages the strategy demands
        self.ma10 = self.I(SMA, self.data.Close, 10)
        self.ma20 = self.I(SMA, self.data.Close, 20)
        self.ma50 = self.I(SMA, self.data.Close, 50)
        self.ma100 = self.I(SMA, self.data.Close, 100)
        
        # Compute daily RSI(30)
        self.daily_rsi = self.I(RSI, self.data.Close, self.d_rsi)
        
        # To construct weekly RSI, we can use `resample_apply()`
        # helper function from the library
        self.weekly_rsi = resample_apply(
            'W-FRI', RSI, self.data.Close, self.w_rsi)
        
        
    def next(self):
        price = self.data.Close[-1]
        
        # If we don't already have a position, and
        # if all conditions are satisfied, enter long.
        if (not self.position and
            self.daily_rsi[-1] > self.level and
            self.weekly_rsi[-1] > self.level and
            self.weekly_rsi[-1] > self.daily_rsi[-1] and
            self.ma10[-1] > self.ma20[-1] > self.ma50[-1] > self.ma100[-1] and
            price > self.ma10[-1]):
            
            # Buy at market price on next open, but do
            # set 8% fixed stop loss.
            self.buy(sl=.92 * price)
        
        # If the price closes 2% or more below 10-day MA
        # close the position, if any.
        elif price < .98 * self.ma10[-1]:
            self.position.close()

/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

from backtesting.test import GOOG

backtest = Backtest(GOOG, System, commission=.002)
backtest.run()

Backtest.run:   0%|          | 0/2048 [00:00<?, ?bar/s]

Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure Time [%]                        2.79
Equity Final [$]                      9939.03
Equity Peak [$]                      10940.25
Commissions [$]                        156.64
Return [%]                              -0.61
Buy & Hold Return [%]                  313.30
Return (Ann.) [%]                       -0.07
Volatility (Ann.) [%]                    4.95
CAGR [%]                                -0.05
Sharpe Ratio                            -0.01
Sortino Ratio                           -0.02
Calmar Ratio                            -0.01
Alpha [%]                               -7.35
Beta                                     0.02
Max. Drawdown [%]                      -10.23
Avg. Drawdown [%]                       -9.54
Max. Drawdown Duration     2653 days 00:00:00
Avg. Drawdown Duration     1410 days 00:00:00
# Trades                                    4
Win Rate [%]                            25.00
Best Trade [%]                           9.91
Worst Trade [%]                         -4.27
Avg. Trade [%]                           0.28
Max. Trade Duration          35 days 00:00:00
Avg. Trade Duration          21 days 00:00:00
Profit Factor                            1.21
Expectancy [%]                           0.43
SQN                                      0.08
Kelly Criterion                          0.03
_strategy                              System
_equity_curve                          Equ...
_trades                      Size  EntryBa...
dtype: object

%%time

backtest.optimize(d_rsi=range(10, 35, 5),
                  w_rsi=range(10, 35, 5),
                  level=range(30, 80, 10))

Backtest.optimize:   0%|          | 0/125 [00:00<?, ?it/s]

Backtest.run:   0%|          | 0/2048 [00:00<?, ?bar/s]

Backtest.run:   0%|          | 0/2048 [00:00<?, ?bar/s]

Backtest.run:   0%|          | 0/2048 [00:00<?, ?bar/s]

Backtest.run:   0%|          | 0/2048 [00:00<?, ?bar/s]

backtest.plot()

Multiple Time Frames¶