feat: an example

auto_trading/broker/backtest.py

@@ -1,3 +1,4 @@
+from os import curdir
 import pandas as pd
 
 from ..interfaces import Broker
@@ -8,10 +9,10 @@ class Backtest(Broker):
     Backtest Broker
     """
     
-    def __init__(self, f_name: str) -> None:
+    def __init__(self, f_name: str, **kwargs) -> None:
         """Read the csv file and store it into a dataframe"""
-        self.data = pd.read_csv(f_name)
-        self.curcor = -1
+        self.data = pd.read_csv(f_name, **kwargs)
+        self.curcor = 0
     
     def __bool__(self):
         return self.curcor < len(self.data)
@@ -19,4 +20,4 @@ class Backtest(Broker):
     def next(self):
         """Return the next row of the dataframe"""
         self.curcor += 1
-        return self.data.iloc[:self.curcor]
+        return self.data.iloc[:self.curcor].copy()

auto_trading/main.py

@@ -23,7 +23,8 @@ class Bot:
     def run_once(self):
         """run the bot once"""
         data = self.broker.next()
-        self.strategy.run(self.ptf, self.predictor.predict(data), data)
+        current_conversion_rate = data.iloc[-1].to_dict()
+        self.strategy.run(self.ptf, self.predictor.predict(data), current_conversion_rate)
 
     def print_results(self):
         """print the results"""

auto_trading/predictor/mean_agg.py

@@ -13,4 +13,5 @@ class MeanAggregator(Predictor):
     
     def predict(self, data: pd.DataFrame) -> dict:
         """Predict from others predictors."""
-        return pd.DataFrame([cls.predict(data) for cls in self.predictors]).mean().to_dict()
+        preds = [cls.predict(data) for cls in self.predictors]
+        return pd.DataFrame(preds).mean().to_dict()

auto_trading/strat/all_in.py

@@ -10,12 +10,13 @@ class AllIn(Strategy):
         """Run the strategy"""
         # first sell all the stocks
         money = 0
-        for stock, amount in filter(lambda kv: kv[1], ptf.content().items()):
-            money += current_conversion_rate[stock]*amount
-            ptf.widraw(stock, amount)
+        for stock, amount in ptf.content().items():
+            #print(f"{stock}: {amount}")
+            if amount > 0:
+                money += current_conversion_rate[stock]*amount
+                ptf.widraw(amount, stock)
         
         # after get the greatest result
         greatest = max(result, key=lambda k: result[k])
-        
         # then buy all the greatest result
-        ptf.deposit(amount/current_conversion_rate[greatest], greatest)
+        ptf.deposit(money/current_conversion_rate[greatest], greatest)

main.py

@@ -0,0 +1,24 @@
+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.main import Bot
+
+if __name__ == '__main__':
+    csv = "data/price_history.csv"
+    with open(csv, 'r') as f:
+        head = f.readline().replace("\n", "").split(",")[1:]
+    
+    pred = MeanAggregator([RandomPredictor() for _ in range(4)])
+    
+    bot = Bot(
+        ptf=InMemoryPortfolio({k:1 for k in head}),
+        strategy=AllIn(),
+        broker=Backtest(csv, index_col=0, skiprows=1600, names=head),
+        predictor=pred
+    )
+    
+    bot.run()
+    
+    bot.print_results()