Add ATR regime backtest filter

atr_regime_backtest.py

@@ -0,0 +1,43 @@
+from datetime import datetime
+from dotenv import load_dotenv
+from os import getenv
+from pandas import concat
+
+from backtesting import backtest_iron_condor
+from backtesting.credit_targeting import create_strategies
+from backtesting.filter import ATRRegimeFilter
+from plotting import BacktestChart, plot
+
+load_dotenv()
+
+if __name__ == '__main__':
+    start_date = datetime(2016, 1, 1)
+    end_date = datetime.now()
+
+    credit_target = float(getenv('CREDIT_TARGET'))
+    entry_times = getenv('ENTRY_TIMES').split(',')
+
+    backtest_results = []
+
+    for entry_time in entry_times:
+        call_spread_strategy, put_spread_strategy = create_strategies(credit_target, entry_time)
+
+        backtest_result = backtest_iron_condor(
+            f'${credit_target:.2f} Iron Condor @ {call_spread_strategy.trade_entry_time}', 
+            call_spread_strategy, 
+            put_spread_strategy, 
+            start_date, 
+            end_date,
+            filters = [ATRRegimeFilter()]
+        )
+
+        backtest_results.append(backtest_result)
+
+    combined_backtest_results = concat(backtest_results)
+    summed_results = combined_backtest_results.groupby('Date')['Cumulative Profit'].sum().reset_index()
+
+    plot(BacktestChart(
+        dates = summed_results['Date'], 
+        profit = summed_results['Cumulative Profit'], 
+        title = f'${credit_target:.2f} Iron Condor (ATR Regime Filter)'
+    ))

backtesting/filter/__init__.py

@@ -1,3 +1,4 @@
+from .atr_regime_filter import ATRRegimeFilter
 from .backtest_filter import BacktestFilter
 from .volatility_regime_filter import VolatilityRegimeFilter
 from .vvix_regime_filter import VVIXRegimeFilter

backtesting/filter/atr_regime_filter.py

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+import numpy as np
+
+from datetime import datetime, timedelta
+from dotenv import load_dotenv
+from os import getenv
+from pandas import concat, DataFrame, Series
+from database.ohlc import ohlc
+
+from .backtest_filter import BacktestFilter
+
+load_dotenv()
+
+class ATRRegimeFilter(BacktestFilter):
+    def __init__(self):
+        self.backtest_filter = self.filter()
+
+    def calculate_atr(self, high, low, close, period = 5):
+        tr = np.maximum(high - low, np.abs(high - close.shift(1)), np.abs(low - close.shift(1)))
+        atr = tr.rolling(window = period).mean()
+        return atr
+
+    def filter(self) -> DataFrame:
+        data_start_date = datetime.strptime(getenv('OPTION_DATA_START_DATE'), '%Y-%m-%d')
+        now = datetime.now()
+        
+        spx_data = ohlc(
+            symbol = 'SPX.XO', 
+            timeframe = '1d', 
+            start_date = data_start_date - timedelta(weeks = 52), 
+            end_date = now
+        )
+        spx_data.rename(columns = {'Timestamp': 'Date'}, inplace = True)
+
+        spx_data['ATR'] = self.calculate_atr(spx_data['High'], spx_data['Low'], spx_data['Close'])
+
+        """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+            Filtering is based on the previous day's close, so the current date can be included even though the 
+            data may not be availble yet.
+
+            This allows for utilizing the filter in live trading to decide whether to trade prior to market open.
+        """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+        spx_data = concat([
+            spx_data,
+            DataFrame({
+                'Date': [datetime.combine(now, datetime.min.time())],
+                'Open': [0.0],
+                'High': [0.0],
+                'Low': [0.0],
+                'Close': [0.0],
+                'Volume': [0.0],
+                'ATR': [0.0]
+            })], 
+            ignore_index = True
+        )
+
+        percent_rank = lambda x: Series(x).rank(pct = True).iloc[-1]
+        spx_data['ATR_Percent_Rank'] = spx_data['ATR'].shift(1).rolling(window = 252).apply(percent_rank)
+
+        filtered_data = spx_data[spx_data['Date'] >= data_start_date].copy()
+        # filtered_data['Trade Allowed'] = (filtered_data['ATR_Percent_Rank'] > 0.25) & (filtered_data['ATR_Percent_Rank'] < 0.75)
+        filtered_data['Trade Allowed'] = filtered_data['ATR_Percent_Rank'] < 0.75
+
+        filtered_data = filtered_data[['Date', 'Trade Allowed']].reset_index(drop = True)
+        return filtered_data

test/filter/atr_regime_filter_test.py

@@ -0,0 +1,4 @@
+from backtesting.filter import ATRRegimeFilter
+
+filter = ATRRegimeFilter()
+print(filter.backtest_filter)