fix: fillna with zeros

auto_trading/main.py

@@ -2,6 +2,9 @@
     One script to rule them all, One script to find them,
     One script to bring them all and in the darkness bind them
 """
+import pandas as pd
+
+
 from .interfaces import Portfolio, Strategy, Broker, Predictor
 
 
@@ -23,7 +26,7 @@ class Bot:
     def run_once(self):
         """run the bot once"""
         data = self.broker.next()
-        current_conversion_rate = data.iloc[-1].to_dict()
+        current_conversion_rate = data.iloc[-1].fillna(0).to_dict()
         self.strategy.run(self.ptf, self.predictor.predict(data), current_conversion_rate)
 
     def print_results(self):

auto_trading/predictor/selector.py

@@ -0,0 +1,14 @@
+import pandas as pd
+
+
+from ..interfaces import Predictor
+
+
+class SelectorPredictor(Predictor):
+    
+    def __init__(self, to_return) -> None:
+        self.to_return = to_return
+    
+    def predict(self, data: pd.DataFrame) -> dict:
+        """It's just random"""
+        return {k:0 for k in data.to_dict().keys()} | self.to_return

auto_trading/strat/all_in.py

@@ -18,5 +18,7 @@ class AllIn(Strategy):
         
         # after get the greatest result
         greatest = max(result, key=lambda k: result[k])
+        
         # then buy all the greatest result
-        ptf.deposit(money/current_conversion_rate[greatest], greatest)
+        to_add = money/current_conversion_rate[greatest]
+        ptf.deposit(to_add, greatest)

main.py

@@ -3,6 +3,7 @@ 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.normalized import NormalizedPredictor
+from auto_trading.predictor.selector import SelectorPredictor
 from auto_trading.main import Bot
 
 if __name__ == '__main__':
@@ -10,7 +11,7 @@ if __name__ == '__main__':
     with open(csv, 'r') as f:
         head = f.readline().replace("\n", "").split(",")[1:]
     
-    pred = MeanAggregator([NormalizedPredictor() for _ in range(4)])
+    pred = MeanAggregator([NormalizedPredictor(), SelectorPredictor({"Tether": 0.7})])
     
     bot = Bot(
         ptf=InMemoryPortfolio({k:1 for k in head}),
@@ -19,6 +20,10 @@ if __name__ == '__main__':
         predictor=pred
     )
     
-    bot.run()
-    
     bot.print_results()
+    print("\n")
+    
+    for _ in range(3):
+        bot.run_once()
+        bot.print_results()
+        print("\n")