feat: normalized agg

auto_trading/main.py

@@ -29,4 +29,4 @@ class Bot:
     def print_results(self):
         """print the results"""
         for k,v in self.ptf.content().items():
-            print(f"{k}: {v}")
+            print(f"{k}: {v:e}")

auto_trading/predictor/normalized.py

@@ -0,0 +1,14 @@
+from random import random
+
+import pandas as pd
+
+
+from ..interfaces import Predictor
+
+
+class NormalizedPredictor(Predictor):
+    def predict(self, data: pd.DataFrame) -> dict:
+        """It's just random"""
+        df = data.iloc[-1]
+        df = df/df.sum()
+        return df.to_dict()

main.py

@@ -2,7 +2,7 @@ from auto_trading.broker.backtest import Backtest
 from auto_trading.strat.all_in import AllIn
 from auto_trading.ptf.in_memory import InMemoryPortfolio
 from auto_trading.predictor.mean_agg import MeanAggregator
-from auto_trading.predictor.random_predictor import RandomPredictor
+from auto_trading.predictor.normalized import NormalizedPredictor
 from auto_trading.main import Bot
 
 if __name__ == '__main__':
@@ -10,7 +10,7 @@ if __name__ == '__main__':
     with open(csv, 'r') as f:
         head = f.readline().replace("\n", "").split(",")[1:]
     
-    pred = MeanAggregator([RandomPredictor() for _ in range(4)])
+    pred = MeanAggregator([NormalizedPredictor() for _ in range(4)])
     
     bot = Bot(
         ptf=InMemoryPortfolio({k:1 for k in head}),