Custom data on a local backtest

To use your personal data files in a QuantConnect algorithm, you need to create a custom data class and modify your algorithm to use this class. Here's how you can update your code:

1. Create a Custom Data Class: Define a class inheriting from PythonData to handle your custom data. Implement the get_source and reader methods to specify the data source and parsing logic.

2. Modify the Algorithm: Use the AddData method to add your custom data class to the algorithm.

Here's an example of how you can implement these steps:

from AlgorithmImports import *
import os
from datetime import datetime, timedelta

class CustomForexData(PythonData):
    def get_source(self, config, date, is_live_mode):
        # Define the path to your data file
        source = os.path.join(Globals.DATA_FOLDER, "forex", f"{config.symbol.value}.csv")
        return SubscriptionDataSource(source, SubscriptionTransportMedium.LOCAL_FILE, FileFormat.CSV)

    def reader(self, config, line, date, is_live_mode):
        # Parse the CSV line
        data = CustomForexData()
        data.symbol = config.symbol

        try:
            csv = line.split(',')
            data.time = datetime.strptime(csv[0], "%Y-%m-%d %H:%M:%S")
            data.value = float(csv[4])  # Assuming the close price is at index 4
            data["Open"] = float(csv[1])
            data["High"] = float(csv[2])
            data["Low"] = float(csv[3])
            data["Close"] = float(csv[4])
            data["Volume"] = float(csv[5])
        except ValueError:
            return None

        return data

class HourlyForexStrategy(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2022, 1, 1)
        self.set_end_date(2023, 1, 1)
        self.set_cash(100000)

        # Add custom data for EURUSD and AUDCAD
        self.eurusd = self.add_data(CustomForexData, "EURUSD", Resolution.HOUR).symbol
        self.audcad = self.add_data(CustomForexData, "AUDCAD", Resolution.HOUR).symbol

        # Initialize moving averages
        self.sma_fast_eurusd = self.sma(self.eurusd, 50, Resolution.HOUR)
        self.sma_slow_eurusd = self.sma(self.eurusd, 200, Resolution.HOUR)

        self.sma_fast_audcad = self.sma(self.audcad, 50, Resolution.HOUR)
        self.sma_slow_audcad = self.sma(self.audcad, 200, Resolution.HOUR)

    def on_data(self, data):
        # EURUSD: Buy if the fast SMA crosses above the slow SMA
        if self.sma_fast_eurusd.current.value > self.sma_slow_eurusd.current.value and not self.portfolio[self.eurusd].invested:
            self.set_holdings(self.eurusd, 0.5)
        elif self.sma_fast_eurusd.current.value < self.sma_slow_eurusd.current.value and self.portfolio[self.eurusd].invested:
            self.liquidate(self.eurusd)

        # AUDCAD: Buy if the fast SMA crosses above the slow SMA
        if self.sma_fast_audcad.current.value > self.sma_slow_audcad.current.value and not self.portfolio[self.audcad].invested:
            self.set_holdings(self.audcad, 0.5)
        elif self.sma_fast_audcad.current.value < self.sma_slow_audcad.current.value and self.portfolio[self.audcad].invested:
            self.liquidate(self.audcad)

Key Points:

• Custom Data Class: The CustomForexData class handles reading your CSV files. Adjust the file path and CSV parsing logic according to your data format.
• Algorithm Initialization: Use AddData to include your custom data in the algorithm.
• Data Handling: Ensure your CSV files are correctly formatted and accessible from the specified path.

This setup should help you run your backtest using personal data files. Adjust the file paths and parsing logic as needed to fit your data structure.

Sources: Custom Data - QuantConnect.com, Key Concepts - QuantConnect.com, Key Concepts - QuantConnect.com, CSV Format Example - QuantConnect.com, Custom Data - QuantConnect.com