gold is working

auto_trading/broker/backtest.py

@@ -9,10 +9,10 @@ class Backtest(Broker):
     Backtest Broker
     """
     
-    def __init__(self, f_name: str, **kwargs) -> None:
+    def __init__(self, f_name: str, start=0, **kwargs) -> None:
         """Read the csv file and store it into a dataframe"""
         self.data = pd.read_csv(f_name, **kwargs)
-        self.curcor = 0
+        self.curcor = start
     
     def __bool__(self):
         return self.curcor < len(self.data)

auto_trading/predictor/derivate.py

@@ -0,0 +1,19 @@
+import pandas as pd
+
+
+from ..interfaces import Predictor
+
+
+class Derivate(Predictor):
+    """Derivate."""
+    
+    def __init__(self, coef: float = 1) -> None:
+        self.coef = coef
+    
+    def predict(self, data: pd.DataFrame) -> dict:
+        """Predict from others predictors."""
+        derivates = data.diff().iloc[-10:]
+        maxs = derivates.abs().max()
+        last = self.coef*derivates.iloc[-1]
+        res = (last/maxs).fillna(0).to_dict()
+        return res

main.py

@@ -5,36 +5,35 @@ from auto_trading.predictor.mean_agg import MeanAggregator
 from auto_trading.predictor.normalized import NormalizedPredictor
 from auto_trading.predictor.selector import SelectorPredictor
 from auto_trading.predictor.random_predictor import RandomPredictor
+from auto_trading.predictor.derivate import Derivate
 from auto_trading.main import Bot
 import matplotlib.pyplot as plt  # type: ignore
 from tqdm import tqdm
-
+import pandas as pd
 
 if __name__ == '__main__':
     csv = "data/gold.csv"
-    bt = Backtest(csv, index_col=0)
+    bt = Backtest(csv, start=10, index_col=0)
     
     start = {"USD":10_000, "Gold": 0}
-    pred = MeanAggregator([RandomPredictor(), SelectorPredictor({"USD": -0.1}), RandomPredictor()])
+    # pred = MeanAggregator([RandomPredictor(), SelectorPredictor({"USD": -0.1}), RandomPredictor()])
+    pred = Derivate(-1)
+    
     
     bot = Bot(
         ptf=InMemoryPortfolio(start.copy()),
         strategy=AllIn(),
         broker=bt,
-        predictor=SelectorPredictor({"USD": -0.1})
+        predictor=pred
     )
 
-    TIME = bt.data.index
-    B = []
-    for _ in tqdm(TIME):
+    data = pd.DataFrame(index=bt.data.index, columns=bt.data.columns)
+    
+    for date in tqdm(data.index):
         bot.run_once()
-        # 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() )
+        data.loc[date] = bot.ptf.content()
     bot.print_results()
     
-    plt.plot(TIME,B)
+    
+    data.plot()
     plt.show()