fbp_current_trade_recs.py

def backtestStockAndPredict(stock, n_days_lst, training_days_lst, mov_avg_lst, 
                            forecast_period_lst, fbp_intervals,stop_early=True, visualize=True):
    
    # Printing the stock
    print(f"\nBacktesting {stock}. . .")

    # Tuning parameters for stock
    results = parameterTuning(
        stock, 
        n_days_lst, 
        training_days_lst, 
        mov_avg_lst, 
        forecast_period_lst, 
        fbp_intervals,
        stop_early=stop_early
    )
    
    if results['optimumResultFound']==False:
        print(f"\t***No optimum params found for {stock}***")
    
    # Optimum Parameters
    opt_params = results['optimumParamLst'][-1]
    
    # Visualizing last (best) result
    if visualize:
        
        # Getting the performance DF
        performance = opt_params['bt_performance']
        
        # Visual of the backtest
        fig = px.line(
            performance,
            x=performance.index,
            y=performance.columns,
            title=f'FBProphet vs Buy&Hold for {stock}',
            labels={"value": "Portfolio Balance",
                    "index": "Date"}
        )

        fig.show()
    
    # Retrieving prices with the given parameters
    prices, price_history = getStockPrices(
        stock, 
        n_days=opt_params['n_days'],
        training_days=opt_params['training_days'], 
        mov_avg=opt_params['mov_avg']
    )
            
    # Run Prophet for current prediction
    preds = fbpTrainPredict(
        prices.tail(opt_params['training_days']), 
        opt_params['forecast_period'],
        opt_params['interval_width']
    ).tail(1)

    preds['Open'] = prices.tail(1)['Open'].values

    # Getting forecast prophet positions
    trade_decision = fbpPositions(preds.to_dict('records')[0], short=True)

    trade_dict = {
        1 : f"Buy {stock}",
        0 : f"Exit {stock}/Do nothing",
        -1: f"Short {stock}"
    }

    # Printing trade decision
    print(trade_dict[trade_decision])
    
    # Printing the optimum params
    print("Best Optimum Parameters Found:\n", opt_params)

    return