Test Trading Performance Based on MACD (Python)

pip install yfinancepip install mplfinance

import yfinance as yfimport mplfinance as mpfimport talib as taimport numpy as npimport pandas as pdimport mathticker_name = 'MSFT'yticker = yf.Ticker(ticker_name)data = yticker.history(period="1y") # max, 1y, 3mo# macddata["macd"], data["macd_signal"], data["macd_hist"] = ta.MACD(data['Close'])# plot macdmacd_plot = mpf.make_addplot(data["macd"], panel=1, color='fuchsia', title="MACD")colors = ['g' if v >= 0 else 'r' for v in data["macd_hist"]]macd_hist_plot = mpf.make_addplot(data["macd_hist"], type='bar', panel=1, color=colors) # color='dimgray'macd_signal_plot = mpf.make_addplot(data["macd_signal"], panel=1, color='b')# buy/sellbuy_signals, sell_signals, signals = detect_macd_signals(data)# plot buy/sellbuy_plot = mpf.make_addplot(buy_signals, type='scatter', marker='v', markersize=100, panel=0)sell_plot = mpf.make_addplot(sell_signals, type='scatter', marker='^', markersize=100, panel=0)# print buy/sell transaction and statsprint_performance_summary(signals)# plot candle chart and allplots = [macd_plot, macd_signal_plot, macd_hist_plot, buy_plot, sell_plot]mpf.plot(data, type='candle', style='yahoo', addplot=plots, title=f"\n{ticker_name}", ylabel='')

def detect_macd_signals(data):    """Use MACD cross-over to decide buy/sell    Args:      data: panda DataFrame with OHLC with MACD data        Return:      buy_signals, sell_signals: for chart plot      signals: buy/sell transaction for summary printing    """    buy_signals = [np.nan]    sell_signals = [np.nan]    signals = []    last_signal = None    for i in range(1, len(data)):        if data['macd_hist'][i-1] < 0 and data['macd_hist'][i] > 0:            price = (data['Open'][i] + data['Close'][i]) / 2            buy_signals.append(price)            last_signal = 'buy'            signals.append({                'date': data.index[i],                'action': 'buy',                'price': price                })                        sell_signals.append(np.nan)        elif data['macd_hist'][i-1] > 0 and data['macd_hist'][i] < 0 and last_signal == 'buy':            price = (data['Open'][i] + data['Close'][i]) / 2            sell_signals.append(price)            last_signal = 'sell'            signals.append({                'date': data.index[i],                'action': 'sell',                'price': price                })                        buy_signals.append(np.nan)        else:            buy_signals.append(np.nan)            sell_signals.append(np.nan)    return buy_signals, sell_signals, signals

def print_performance_summary(signals):    """Print buy/sell transactions and statistics        Args:      signals: recorded buy/sell transactions    """    pairs = zip(*[iter(signals)]*2)    rows = []    profit_count = 0    profit_pct_avg = 0    for (buy, sell) in pairs:        profit = sell['price'] - buy['price']        profit_pct = profit / buy['price']        if profit > 0:            profit_count += 1        profit_pct_avg += profit_pct        row = {            'buy_date': buy['date'],            'duration':  (sell['date'] - buy['date']).days,            'profit': profit,            'profit_pct': "{0:.2%}".format(profit_pct)        }        rows.append(row)    df =  pd.DataFrame(rows, columns=['buy_date', 'duration', 'profit', 'profit_pct'])    with pd.option_context('display.max_rows', None, 'display.max_columns', None):        print(df)    total_transaction = math.floor(len(signals) / 2)    stats = {        'total_transaction': total_transaction,        'profit_rate': "{0:.2%}".format(profit_count / total_transaction),        'avg_profit_per_transaction': "{0:.2%}".format(profit_pct_avg / total_transaction)    }    for key, value in stats.items():        print('{0:30}  {1}'.format(key, value))

    buy_date  duration     profit profit_pct
0 2020-01-09        18  -0.248023     -0.15%
1 2020-01-30        22   7.896236      4.59%
2 2020-03-26        48  28.745260     18.98%
3 2020-06-09         6  -2.134837     -1.13%
4 2020-06-16        29  15.532458      8.06%
5 2020-08-04         7  -8.439593     -3.96%
6 2020-08-20        15   2.615005      1.23%
7 2020-09-29        24   7.324997      3.52%

total_transaction               8
profit_rate                     62.50%
avg_profit_per_transaction      3.89%