Meta Analysis

To get the results of a backtest, call the ReadBacktestread_backtest 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.read_backtest(project_id, backtest_id)

The following table provides links to documentation that explains how to get the project Id and backtest Id, depending on the platform you use:

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

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

1. Get the backtest orders.

orders = api.read_backtest_orders(project_id, backtest_id)

The following table provides links to documentation that explains how to get the project Id and backtest Id, depending on the platform you use:

Platform	Project Id	Backtest Id
Cloud Platform	Get Project Id	Get Backtest Id
Local Platform	Get Project Id	Get Backtest Id
CLI	Get Project Id	Get Backtest Id

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

2. 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 [x.order for x 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.last_fill_time.date())
       (order_data.buy_fill_prices if is_buy else order_data.sell_fill_prices).append(order.price)

3. 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():
       if order_data.buy_fill_times:
           start_date = min(start_date, min(order_data.buy_fill_times))
           end_date = max(end_date, max(order_data.buy_fill_times))
       if order_data.sell_fill_times:
           start_date = min(start_date, min(order_data.sell_fill_times))
           end_date = max(end_date, max(order_data.sell_fill_times))
   start_date -= timedelta(days=3)
   all_history = qb.history(list(order_data_by_symbol.keys()), start_date, end_date, Resolution.DAILY)

4. 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()



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.

Follow these steps to plot the equity curve, benchmark, and drawdown of a backtest:

1. Get the backtest instance.

backtest = api.read_backtest(project_id, backtest_id)

The following table provides links to documentation that explains how to get the project Id and backtest Id, depending on the platform you use:

Platform	Project Id	Backtest Id
Cloud Platform	Get Project Id	Get Backtest Id
Local Platform	Get Project Id	Get Backtest Id
CLI	Get Project Id	Get Backtest Id

2. Get the "Strategy Equity", "Drawdown", and "Benchmark" Chart objects.

equity_chart = backtest.charts["Strategy Equity"]
drawdown_chart = backtest.charts["Drawdown"]
benchmark_chart = backtest.charts["Benchmark"]

3. Get the "Equity", "Equity Drawdown", and "Benchmark" Series from the preceding charts.

equity = equity_chart.series["Equity"].values
drawdown = drawdown_chart.series["Equity Drawdown"].values
benchmark = benchmark_chart.series["Benchmark"].values

4. Create a pandas.DataFrame from the series values.

df = pd.DataFrame({
    "Equity": pd.Series({value.TIME: value.CLOSE for value in equity}),
    "Drawdown": pd.Series({value.TIME: value.Y for value in drawdown}),
    "Benchmark": pd.Series({value.TIME: value.Y for value in benchmark})
}).ffill()

5. Plot the performance chart.

# Create subplots to plot series on same/different plots
fig, ax = plt.subplots(2, 1, figsize=(12, 12), sharex=True, gridspec_kw={'height_ratios': [2, 1]})

# Plot the equity curve
ax[0].plot(df.index, df["Equity"])
ax[0].set_title("Strategy Equity Curve")
ax[0].set_ylabel("Portfolio Value ($)")

# Plot the benchmark on the same plot, scale by using another y-axis
ax2 = ax[0].twinx()
ax2.plot(df.index, df["Benchmark"], color="grey")
ax2.set_ylabel("Benchmark Price ($)", color="grey")

# Plot the drawdown on another plot
ax[1].plot(df.index, df["Drawdown"], color="red")
ax[1].set_title("Drawdown")
ax[1].set_xlabel("Time")
ax[1].set_ylabel("%")

The following table shows all the chart series you can plot:

Follow these steps to display the insights of each asset in a backtest:

1. Get the insights.

insight_response = api.read_backtest_insights(project_id, backtest_id)

The following table provides links to documentation that explains how to get the project Id and backtest Id, depending on the platform you use:

Platform	Project Id	Backtest Id
Cloud Platform	Get Project Id	Get Backtest Id
Local Platform	Get Project Id	Get Backtest Id
CLI	Get Project Id	Get Backtest Id

The read_backtest_insights method returns an InsightResponse object, which have the following properties:

2. Organize the insights into a DataFrame.

import pytz

def _eastern_time(unix_timestamp):
    return unix_timestamp.replace(tzinfo=pytz.utc)\
        .astimezone(pytz.timezone('US/Eastern')).replace(tzinfo=None)

insight_df = pd.DataFrame(
    [
        {
            'Symbol': i.symbol,
            'Direction': i.direction,
            'Generated Time': _eastern_time(i.generated_time_utc),
            'Close Time': _eastern_time(i.close_time_utc),
            'Weight': i.weight
        }
        for i in insight_response.insights
    ]
)

3. Get the price history of each security that has an insight.

symbols = list(insight_df['Symbol'].unique())
qb = QuantBook()
history = qb.history(
    symbols, insight_df['Generated Time'].min()-timedelta(1), 
    insight_df['Close Time'].max(), Resolution.DAILY
)['close'].unstack(0)

4. Plot the price and insights of each asset.

colors = ['yellow', 'green', 'red']
fig, axs = plt.subplots(len(symbols), 1, sharex=True)
for i, symbol in enumerate(symbols):
    ax = axs[i]
    history[symbol].plot(ax=ax)
    for _, insight in insight_df[insight_df['Symbol'] == symbol].iterrows():
        ax.axvspan(
            insight['Generated Time'], insight['Close Time'], 
            color=colors[insight['Direction']], alpha=0.3
        )
    ax.set_title(f'Insights for {symbol.value}')
    ax.set_xlabel('Date')
    ax.set_ylabel('Price')
plt.tight_layout()
plt.show()