Visualisation

auto_trading/indicators/slopy.py

@@ -25,35 +25,26 @@ class Slopy(Indicator):
                 For each stock give -1 if realy bad and +1 if realy good.
         """
 
-        #print(data.tail())
-        #print(data.index.names)
-
-        #print(data["date"])
-
-        #subdata = data["high"]
-
         # only use date as index => actions become columns
         data2 = data.unstack()
 
         # select high prices for each action
-        highData = data2["high"]
-
-        #print(highData.tail())
-
-        #print(highData.last("2D"))
+        highData = data2["close"]
 
         # select only last two days
-        last2D = highData.last("2D")
+        lastND = highData.last("2D")
 
-        res = {}
-        for column in last2D.columns:
-            #pour chaque type d'action
-            #print(column)
-            if len(last2D.index) > 1:
-                #print(last2D[column].get(last2D.index[-1]), last2D[column].get(last2D.index[-2]))
+        # lastND.rolling(window=2).mean()
 
-                ultieme = last2D[column].get(last2D.index[-1])
-                penultieme = last2D[column].get(last2D.index[-2])
+        res = {}
+        for column in lastND.columns:
+            # pour chaque type d'action
+            # print(column)
+            if len(lastND.index) > 1:
+                # print(last2D[column].get(last2D.index[-1]), last2D[column].get(last2D.index[-2]))
+
+                ultieme = lastND[column].get(lastND.index[-1])
+                penultieme = lastND[column].get(lastND.index[-2])
 
                 if ultieme > penultieme:
                     res[column] = 1

auto_trading/strat/buyupselldown.py

@@ -9,10 +9,9 @@ from ..interfaces import Indicator, Strategy, Order, PTFState
 class BuyUpSellDown(Strategy):
     """Just hold some stock."""
 
-    def __init__(self, to_hold: str, indicators: Dict[str, Indicator]):
+    def __init__(self, indicators: Dict[str, Indicator]):
         """Init the class"""
         super().__init__(indicators=indicators)
-        self.to_hold = to_hold
 
     def execute(
         self, data: pd.DataFrame, indicators_results: pd.DataFrame, state: PTFState
@@ -35,10 +34,12 @@ class BuyUpSellDown(Strategy):
         orders = []
         # if I have some money
         for stock_name in indicators_results.columns:
-            #for each stock
+            # for each stock
             if (balance := state.balance) > 0:
                 # I calculate the value of the stock
-                market_price = data.loc[data.index[-1][0]].close.to_dict().get(stock_name)
+                market_price = (
+                    data.loc[data.index[-1][0]].close.to_dict().get(stock_name)
+                )
 
                 trust = int(indicators_results[stock_name])
                 print(trust)
@@ -46,14 +47,18 @@ class BuyUpSellDown(Strategy):
                 if market_price is None:
                     # retry later
                     return []
-                
+
                 amount = balance / (market_price * nb_stock_type)
 
                 if trust > 0:
                     # and I buy it all at market price
-                    orders.append(Long(stock=stock_name, amount=amount, price=market_price))
+                    orders.append(
+                        Long(stock=stock_name, amount=amount, price=market_price)
+                    )
                 elif trust < 0:
                     # and I sell it all at market price
-                    orders.append(Short(stock=stock_name, amount=amount, price=market_price))
+                    orders.append(
+                        Short(stock=stock_name, amount=amount, price=market_price)
+                    )
 
         return orders  # type: ignore

main.py

@@ -1,3 +1,6 @@
+from matplotlib import lines
+import matplotlib.pyplot as plt
+
 from auto_trading.broker.backtest import Backtest
 from auto_trading.indicators.dumb import Dumb
 from auto_trading.indicators.slopy import Slopy
@@ -9,19 +12,36 @@ from auto_trading.bot import Bot
 
 
 if __name__ == "__main__":
-    bt = Backtest("./data/NYSE_smallest.csv")
+    bt = Backtest("./data/NYSE_small.csv")
     ptf = InMemoryPortfolio(
         base_balance=100, change_rate_getter=lambda: bt.current_change
     )
 
-    #strategy = Hold("GOOGL", {"dumb": Dumb(bt.data.close)})
-    strategy = BuyUpSellDown("GOOGL", {"slopy": Slopy()})
+    # strategy = Hold("GOOGL", {"dumb": Dumb(bt.data.close)})
+    strategy = BuyUpSellDown({"slopy": Slopy()})
 
     bot = Bot(ptf, strategy, bt)
 
     bot.run()
 
+    ###### Visualisation ######
+
+    data = bt.data.unstack()
+
+    # select high prices for each action
+    closeData = data["close"]
+
+    sma = closeData.rolling(window=5).mean()
+    ema = closeData.ewm(alpha=0.6).mean()
+
+    plt.plot(closeData, label="real", marker="x", linewidth=0)
+    plt.plot(sma, color="red", label="SMA")
+    plt.plot(ema, color="green", label="EMA")
+    plt.legend()
+
     for order in ptf.history:
         print(order)
 
     print(bot.ptf.total_balance(bot.broker.current_change))
+
+    plt.show()