Synced repo using 'sync_with_huggingface' Github Action

app.py

@@ -23,7 +23,6 @@ def run(stock, interval, period, strategy, swing_hl, ema1=9, ema2=21, cross_clos
 
     backtest_plot = gr.Plot()
 
-
     # Plotting backtest plot based on strategy.
     if strategy == "Order Block":
         backtest_plot = smc_plot_backtest(data, 'test.html', swing_hl)

strategies.py

@@ -150,6 +150,7 @@ class SMCStructure(TrailingStrategy):
         swings = swings[~np.isnan(swings['Level'])]
         return swings['Level'].iloc[-2]
 
+strategies = {'Order Block': SMC_test, 'Order Block with EMA': SMC_ema , 'Structure trading': SMCStructure}
 
 if __name__ == "__main__":
     from utils import fetch

utils.py

@@ -1,5 +1,7 @@
 import yfinance as yf
 from backtesting import Backtest
+import pandas as pd
+import random
 
 from strategies import SMC_test, SMC_ema, SMCStructure
 
@@ -32,6 +34,95 @@ def smc_ema_backtest(data, ema1, ema2, closecross, **kwargs):
 def smc_structure_backtest(data, swing_hl, **kwargs):
     return Backtest(data, SMCStructure, **kwargs).run(swing_hl=swing_hl)
 
+def random_test(strategy: str, period: str, interval: str, no_of_stocks: int = 5, **kwargs):
+    nifty50 = pd.read_csv("data/ind_nifty50list.csv")
+    ticker_list = pd.read_csv("data/Ticker_List_NSE_India.csv")
+
+    # Merging nifty50 and ticker_list dataframes to get 'YahooEquiv' column.
+    nifty50 = nifty50.merge(ticker_list, "inner", left_on=['Symbol'], right_on=['SYMBOL'])
+
+    # Generating random indices between 0 and len(nifty50).
+    random_indices = random.sample(range(0, len(nifty50)), no_of_stocks)
+
+    df = pd.DataFrame()
+
+    for i in random_indices:
+        # Fetching ohlc of random ticker_symbol.
+        ticker_symbol = nifty50['YahooEquiv'].values[i]
+        data = fetch(ticker_symbol, period, interval)
+
+        if strategy == "Order Block":
+            backtest_results = smc_backtest(data, kwargs['swing_hl'])
+        elif strategy == "Order Block with EMA":
+            backtest_results = smc_ema_backtest(data, kwargs['ema1'], kwargs['ema2'], kwargs['cross_close'])
+        elif strategy == "Structure trading":
+            backtest_results = smc_structure_backtest(data, kwargs['swing_hl'])
+        else:
+            raise Exception('Strategy not found')
+
+        # Converting pd.Series to pd.Dataframe
+        backtest_results = backtest_results.to_frame().transpose()
+
+        backtest_results['stock'] = ticker_symbol
+
+        # Reordering columns.
+        # cols = df.columns.tolist()
+        # cols = cols[-1:] + cols[:-1]
+        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [$]', 'Buy & Hold Return [%]', '# Trades', 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']
+        backtest_results = backtest_results[cols]
+
+        df = pd.concat([df, backtest_results])
+
+    df = df.sort_values(by=['Return [%]'], ascending=False)
+
+    return df
+
+def complete_test(strategy: str, period: str, interval: str, **kwargs):
+    nifty50 = pd.read_csv("data/ind_nifty50list.csv")
+    ticker_list = pd.read_csv("data/Ticker_List_NSE_India.csv")
+
+    # Merging nifty50 and ticker_list dataframes to get 'YahooEquiv' column.
+    nifty50 = nifty50.merge(ticker_list, "inner", left_on=['Symbol'], right_on=['SYMBOL'])
+
+    df = pd.DataFrame()
+
+    for i in range(len(nifty50)):
+        # Fetching ohlc of random ticker_symbol.
+        ticker_symbol = nifty50['YahooEquiv'].values[i]
+        data = fetch(ticker_symbol, period, interval)
+
+        if strategy == "Order Block":
+            backtest_results = smc_backtest(data, kwargs['swing_hl'])
+        elif strategy == "Order Block with EMA":
+            backtest_results = smc_ema_backtest(data, kwargs['ema1'], kwargs['ema2'], kwargs['cross_close'])
+        elif strategy == "Structure trading":
+            backtest_results = smc_structure_backtest(data, kwargs['swing_hl'])
+        else:
+            raise Exception('Strategy not found')
+
+        # Converting pd.Series to pd.Dataframe
+        backtest_results = backtest_results.to_frame().transpose()
+
+        backtest_results['stock'] = ticker_symbol
+
+        # Reordering columns.
+        # cols = df.columns.tolist()
+        # cols = cols[-1:] + cols[:-1]
+        cols = ['stock', 'Start', 'End', 'Return [%]', 'Equity Final [$]', 'Buy & Hold Return [%]', '# Trades', 'Win Rate [%]', 'Best Trade [%]', 'Worst Trade [%]', 'Avg. Trade [%]']
+        backtest_results = backtest_results[cols]
+
+        df = pd.concat([df, backtest_results])
+
+    df = df.sort_values(by=['Return [%]'], ascending=False)
+
+    return df
+
+
 if __name__ == "__main__":
+    # random_testing("")
     # data = fetch('RELIANCE.NS', period='1y', interval='15m')
-    df = yf.download('RELIANCE.NS', period='1yr', interval='15m')
+    # df = yf.download('RELIANCE.NS', period='1yr', interval='15m')
+
+    rt = all_testing("Order Block", '1mo', '15m', swing_hl=20)
+    rt.to_excel('test/all_testing_1.xlsx', index=False)
+    print(rt)