Meta Analysis

Backtest Analysis

Introduction

Load your backtest results into the Research Environment to analyze trades and easily compare them against the raw backtesting data. Compare backtests from different projects to find uncorrelated strategies to combine for better performance.

Loading your backtest trades allows you to plot fills against detailed data, or locate the source of profits. Similarly you can search for periods of high churn to reduce turnover and trading fees.

Read Backtest Results

To get the results of a backtest, call the ReadBacktest method with the project Id and backtest ID.

#load "../Initialize.csx"
#load "../QuantConnect.csx"

using QuantConnect;
using QuantConnect.Api;

var backtest = api.ReadBacktest(projectId, backtestId);
backtest = api.ReadBacktest(project_id, backtest_id)

To get the project Id, open the project in the Algorithm Lab and check the URL. For example, the project Id of https://www.quantconnect.com/project/13946911 is 13946911.

To get the backtest Id, open a backtest result in the Algorithm Lab and check the last line of its log file. An example backtest Id is 97e7717f387cadd070e4b77015aacece.

Note that this method returns a snapshot of the backtest at the current moment. If the backtest is still executing, the result won't include all of the backtest data.

The ReadBacktest method returns a Backtest object, which have the following attributes:

Plot Order Fills

Follow these steps to plot the daily order fills of a backtest:

  • Get the backtest orders.
  • orders = api.ReadBacktestOrders(project_id, backtest_id)

    To get the project Id, open the project in the Algorithm Lab and check the URL. For example, the project Id of https://www.quantconnect.com/project/13946911 is 13946911.

    To get the backtest Id, open a backtest result in the Algorithm Lab and check the last line of its log file. An example backtest Id is 97e7717f387cadd070e4b77015aacece.

    The ReadBacktestOrders method returns a list of Order objects, which have the following properties:

  • Organize the trade times and prices for each security into a dictionary.
    class OrderData:
        def __init__(self):
            self.buy_fill_times = []
            self.buy_fill_prices = []
            self.sell_fill_times = []
            self.sell_fill_prices = []
    
    order_data_by_symbol = {}
    for order in orders:
        if order.Symbol not in order_data_by_symbol:
            order_data_by_symbol[order.Symbol] = OrderData()
        order_data = order_data_by_symbol[order.Symbol]
        is_buy = order.Quantity > 0
        (order_data.buy_fill_times if is_buy else order_data.sell_fill_times).append(order.LastFillTime.date())
        (order_data.buy_fill_prices if is_buy else order_data.sell_fill_prices).append(order.Price)
  • Get the price history of each security you traded.
    qb = QuantBook()
    start_date = datetime.max.date()
    end_date = datetime.min.date()
    for symbol, order_data in order_data_by_symbol.items():
        start_date = min(start_date, min(order_data.buy_fill_times), min(order_data.sell_fill_times))
        end_date = max(end_date, max(order_data.buy_fill_times), max(order_data.sell_fill_times))
    start_date -= timedelta(days=1)
    all_history = qb.History(list(order_data_by_symbol.keys()), start_date, end_date, Resolution.Daily)
  • Create a candlestick plot for each security and annotate each plot with buy and sell markers.
    import plotly.express as px
    import plotly.graph_objects as go
    
    for symbol, order_data in order_data_by_symbol.items():
        history = all_history.loc[symbol]
    
        # Plot security price candlesticks
        candlestick = go.Candlestick(x=history.index,
                                    open=history['open'],
                                    high=history['high'],
                                    low=history['low'],
                                    close=history['close'],
                                    name='Price')
        layout = go.Layout(title=go.layout.Title(text=f'{symbol.Value} Trades'),
                        xaxis_title='Date',
                        yaxis_title='Price',
                        xaxis_rangeslider_visible=False,
                        height=600)
        fig = go.Figure(data=[candlestick], layout=layout)
    
        # Plot buys
        fig.add_trace(go.Scatter(
            x=order_data.buy_fill_times,
            y=order_data.buy_fill_prices,
            marker=go.scatter.Marker(color='aqua', symbol='triangle-up', size=10),
            mode='markers',
            name='Buys',
        ))
    
        # Plot sells
        fig.add_trace(go.Scatter(
            x=order_data.sell_fill_times,
            y=order_data.sell_fill_prices,
            marker=go.scatter.Marker(color='indigo', symbol='triangle-down', size=10),
            mode='markers',
            name='Sells',
        ))
    
    fig.show()
  • Plot of AAPL price with buy/sell markers Plot of SPY price with buy/sell markers

    Note: The preceding plots only show the last fill of each trade. If your trade has partial fills, the plots only display the last fill.

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