all in strat

main.py

@@ -7,35 +7,34 @@ from auto_trading.predictor.selector import SelectorPredictor
 from auto_trading.predictor.random_predictor import RandomPredictor
 from auto_trading.main import Bot
 import matplotlib.pyplot as plt  # type: ignore
+from tqdm import tqdm
 
 
 if __name__ == '__main__':
     csv = "data/gold.csv"
+    bt = Backtest(csv, index_col=0)
     
     start = {"USD":10_000, "Gold": 0}
-    pred = MeanAggregator([NormalizedPredictor(), SelectorPredictor({"USD": -0.1}), RandomPredictor()])
+    pred = MeanAggregator([RandomPredictor(), SelectorPredictor({"USD": -0.1}), RandomPredictor()])
     
     bot = Bot(
         ptf=InMemoryPortfolio(start.copy()),
         strategy=AllIn(),
-        broker=Backtest(csv, index_col=0),
-        predictor=pred
+        broker=bt,
+        predictor=SelectorPredictor({"USD": -0.1})
     )
-    
-    bot.print_results()
-    print("\n")
 
-    TIME = range(400)
+    TIME = bt.data.index
     B = []
-    for _ in TIME:
+    for _ in tqdm(TIME):
         bot.run_once()
-        #bot.print_results()
-        assets = bot.ptf.content()
-        spread=1/100
-        for k in assets.keys():
-            assets[k]=(1-spread)*assets[k]
+        # bot.print_results()
+        # assets = bot.ptf.content()
+        # spread=1/100
+        # for k in assets.keys():
+        #     assets[k]=(1-spread)*assets[k]
         B.append( bot.balance() )
-        print(B[-1])
-        print("\n")
+    bot.print_results()
+    
     plt.plot(TIME,B)
     plt.show()

requirements.txt

@@ -1,3 +1,4 @@
 matplotlib
 numpy
 pandas
+tqdm