def trade(self): """ Simulate trading based on the moving average strategy Returns: pd.DataFrame: DataFrame containing new columns for: - trading signals - positions - shares - cash - transaction costs - returns - cumulative returns """ data = self.data.copy() data.index = pd.to_datetime(data.index) data["SMA"] = self.SMA() # Initialize portfolio, shares, cash, and transaction cost data["Position"] = 0 data["Shares"] = 0 data["Tran_Cost"] = 0 data["Cash"] = 0 data["Cash"].iloc[0] = self.init data["Position"].iloc[0] = self.init # check if price is above or below moving average using pandas data["Signal"] = np.where(data["Close"] > data["SMA"], 1, 0) data["Signal"] = data["Signal"].diff().fillna(0) data["Buy"] = np.where(data["Signal"] == 1, data["Close"], np.nan) data["Sell"] = np.where(data["Signal"] == -1, data["Close"], np.nan) # Calculate shares and cash for i in range(1, len(data)): if data["Signal"].iloc[i] == 1: # buy as many shares whole (non decimal) as funds allow data["Shares"].iloc[i] = data["Cash"].iloc[i - 1] // (data["Close"].iloc[i] * (1 + self.tran_cost)) data["Tran_Cost"].iloc[i] = data["Shares"].iloc[i] * data["Close"].iloc[i] * self.tran_cost data["Cash"].iloc[i] = data["Cash"].iloc[i - 1] - data["Shares"].iloc[i] * data["Close"].iloc[i] - data["Tran_Cost"].iloc[i] data["Position"].iloc[i] = data["Shares"].iloc[i] * data["Close"].iloc[i] + data["Cash"].iloc[i] elif data["Signal"].iloc[i] == -1: data["Cash"].iloc[i] = data["Cash"].iloc[i - 1] + data["Shares"].iloc[i - 1] * data["Close"].iloc[i] * (1 - self.tran_cost) data["Shares"].iloc[i] = 0 data["Position"].iloc[i] = data["Cash"].iloc[i] else: data["Cash"].iloc[i] = data["Cash"].iloc[i - 1] data["Shares"].iloc[i] = data["Shares"].iloc[i - 1] data["Position"].iloc[i] = data["Shares"].iloc[i] * data["Close"].iloc[i] + data["Cash"].iloc[i] # Calculate returns and cumulative returns data["Returns"] = data["Position"].pct_change().fillna(0) data["Cum_Returns"] = (1 + data["Returns"]).cumprod() # index returns for comparison data["Index_Returns"] = data["Close"].pct_change().fillna(0) data["Index_Cum_Returns"] = (1 + data["Index_Returns"]).cumprod() self.data = data return data

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