The backtest result is designed to give the user full access to all the information produced in a backtest. This creates a pipeline allowing the user rapid iteration or neural network training on outputs.
Creation
The BacktestResult is always returned by a successful strategy.backtest() call.
Functions
get_account_history() -> pd.DataFrame
Response
| Description | Examples | Type |
|---|
| The account history DataFrame contains the user account values at every strategy function call. There is a row for queries for any asset and variable timings. Do not rely on this DataFrame having homogenous time spacing between each row, if using metrics & indicators, evaluate on the resampled account value DataFrame. | 0 2.000000 100000.00 1.599354e+09 120266.140000 | pd.DataFrame |
get_returns() -> pd.DataFrame
Response
| Description | Examples | Type |
|---|
This returns a DataFrame with a time and value column. The epoch times on this DataFrame are resampled to follow the resample_account_value_for_metrics setting in backtest.json. The first value will always be NaN because no change can ever be calculated for the first value. This is useful for indicators & metrics. | 0 1.599354e+09 NaN | pd.DataFrame |
get_resampled_account() -> pd.DataFrame
Response
| Description | Examples | Type |
|---|
This returns a DataFrame similar to that returned by get_account_history(). However, the epoch times on this DataFrame are resampled to follow the resample_account_value_for_metrics setting in backtest.json. This is useful for indicators & metrics. | 0 1.599354e+09 NaN | pd.DataFrame |
get_user_callback_results() -> dict
Response
| Description | Examples | Type |
|---|
| Get the results of any callback defined by the user as a dictionary. The key/value pairs will be organized by the user function name and the value returned | {'custom_function': 2.51341} | dict |
get_metrics() -> dict
Response
| Description | Examples | Type |
|---|
| Get a dictionary with key/value pairs, each representing built in blankly metrics valuations. These keys will always be added unless a failure occurs during the calculation. | {'cagr': 0.647633, 'sortino': 1.24808} | dict |