BtEngine

BtEngine is a backtesting module designed to accept close prices outputted by yfinance.Ticker or yfinance.Tickers. It employs a strategy stored in BtStrategy and iterates over the historical to test the performance of a portfolio when actively trading.

__init__ Arguments

• portfolio_close -> pd.DataFrame: Close prices returned from yfinance
• strategy -> BtStrategy: BtStrategy object to handle signal generation
• hi_lo -> tuple[pandas.DataFrame, pandas.DataFrame]: Tuple of high and low price data from yfinance
• vol -> pandas.DataFrame: Volume data from yfinance

Attributes

• cash -> float: Liquid assets (initial balance = 100,000)
• max_trade_proportion -> float: Maximum trade amount as a percentage of total holdings (default = 0.33)
• price_data -> pandas.DataFrame: Close prices from yfinance
• dt_index: pandas.DateIndex: Default index of the close price data
• hi -> pandas.DataFrame: High prices from yfinance
• lo -> pandas.DataFrame: Low prices from yfinance
• vol -> pandas.DataFrame: Volume data from yfinance
• sma_period -> int: Window for Short-Moving-Average (SMA)
• lma_period -> int: Window for Long-Moving-Average (LMA)
• strat: BtStrategy: Strategy container for signal generation
• holdings: dict[str, float]: Holdings for each stock (in shares)
• buy -> dict[str, list[tuple[str, float]]]: dict of each period (keys) where a buy signal was produced and a list tuples with format (stock_that_produced_buy_sig, strat_score)
• sell -> dict[str, list[tuple[str, float]]]: dict of each period (keys) where a sell signal was produced and a list tuples with format (stock_that_produced_buy_sig, strat_score)
• total_buys -> int: Count of total buy signals
• total_sells -> int: Count of total sell signals

Methods

set_holdings

Method to set holdings using portfolio weights before the run.

Params: - weights -> dict[str, float]: dict of stock symbols and their weights - inv_percent -> float: percentage of starting cash balance to invest in the portfolio

Returns:

• None

run

Run the engine to perform iterative backtesting with the pre-determined strategy

Params: - None

Returns:

• dict[str, float | dict]: dict containing post-run portfolio value in USD, total liquid assets, and asset distribution as a dict of stocks and their respective holdings at the end of the run.

plot_history

Display a matplotlib.pyplot figure that contains key information regarding the performance of the selected backtesting strategy and portfolio.

Params: - None

Returns: - None