80 strategies validations metrics

commons/validation/benchmark.py

@@ -0,0 +1,59 @@
+from backtesting import Backtest, Strategy
+
+import warnings
+import pandas as pd
+import numpy as np
+
+
+np.seterr(divide='ignore')
+
+
+def func_benchmark(dataset, cash, time_tag):
+    df = dataset.df
+    data = df.resample(f'{time_tag}').agg({
+        'Open': 'first',
+        'High': 'max',
+        'Low': 'min',
+        'Close': 'last'})
+    data["B"] = data['Open'] <= data['Open'].shift(-1)
+
+    if data.loc[data.index[-1], f'Open'] < data.loc[data.index[-1], f'Close']:
+        data.loc[data.index[-1], 'B'] = 1
+    else:
+        data.loc[data.index[-1], 'B'] = 0
+
+    y_pred = data["B"].shift(0)
+    y_pred = pd.concat([y_pred, y_pred.iloc[:1]])
+    data = pd.concat([df.iloc[:2], data])
+
+    class benchmark(Strategy):
+        def init(self):
+            self.id = 0
+
+        def next(self):
+            self.position.close()
+            pred = y_pred[self.id]
+            if pred > 0.5:
+                self.buy()
+            elif pred < 0.5:
+                self.sell()
+            self.id += 1
+
+    bt = Backtest(data,
+                  benchmark,
+                  cash=cash,
+                  commission=0,
+                  exclusive_orders=True)
+
+    stats = bt.run()
+    return stats['Equity Final [$]']
+
+
+BENCHMARK = ['10y', '1y', '2Q', '1Q', '1m', '1w']
+
+
+def benchmark(df, cash, equity):
+    for params in BENCHMARK:
+        if equity < func_benchmark(df, cash, params):
+            return params
+            break

commons/validation/cagr.py

@@ -0,0 +1,2 @@
+def cagr_ratio(equity_strat, equity_end, time):
+    return ((equity_end / equity_strat)**(1 / time) - 1) * 100

model/steps/run_backtesting.py

@@ -1,10 +1,14 @@
 import json
 import logging
+import pandas as pd
 
 from backtesting import Backtest, Strategy
 
 from commons.drivepath import cache
 from commons.timing import step
+from commons.validation.cagr import cagr_ratio
+from commons.validation.benchmark import benchmark
+from commons.data.utils import log_change
 
 LOGGER = logging.getLogger(__name__)
 
@@ -38,6 +42,15 @@ def run_backtesting(
         trade_on_close=True)
 
     backtest_results = bt.run()
+    benchmark_lvl = benchmark(dataset,
+                              starting_cash,
+                              backtest_results['Equity Final [$]'])
+    cagr = cagr_ratio(starting_cash,
+                      backtest_results['Equity Final [$]'],
+                      backtest_results['Duration'].days / 365.25)
+    backtest_results = pd.DataFrame(backtest_results)
+    #backtest_results["CAGR Ratio"] = cagr
+    #backtest_results["Benchmark"] = benchmark_lvl
     LOGGER.info(backtest_results)
     bt.plot(resample=False)
     return dict(backtest=bt, backtest_results=backtest_results)