My Stock Trading Journey: Backtesting My planned MACD Trading Strategy

As promised, I will post here the python code I used to back test my trading strategy using MACD indicator.

After I run the code and visualize it resulted in the following figure:

As can be seen from the graph, Using historical data from Jan 1, 2022 up to Oct 19, 2022, my python code program generated 2 signals and to compute the income earned:

Buy Signal:

Entry: Feb 23, 2022 - $254.68

Exit: March 3, 2022 - $279.63

Profit: $279.63 - 254.68 = $25.03

Sell Signal:

Entry: March 3, 2022 - $279.63

Current Price: (Oct 19, 2022) : $220.19

Profit: $279.63 - $220.19 = $59.44

Total Profit: $25.03 + $59.44 = $84.47

The initial code I implemented yield some profit($84.47/$220.19) * 100 = 38.36%). The reason why it was not able to capture the golden opportunities during the period is historical data never met the stop conditions I implemented. It will still need some revision to optimize the profit. But on the contrary, 38.36% is huge profit. And it is just for 1 share. Way better than putting the money in the bank which mostly offer 4% per year and there will be auto deduction of 20% on the earned interest.

Here is the code:

import pandas_ta as ta
import yfinance as yf
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import datetime as dt
start = dt.datetime(2022, 1, 1)
end = dt.datetime.now()

df = yf.Ticker('TSLA').history(start=start,  end=end)[map(str.title, ['open', 'close', 'low', 'high', 'volume'])]

df.ta.macd(close='close', fast=12, slow=26, append=True)

df.columns = [x.lower() for x in df.columns]

MACD_Buy=[]
MACD_Sell=[]

position=False
risk = 0.025
for i in range(0, len(df)):     
        if df['macd_12_26_9'][i] < df['macds_12_26_9'][i] and df['macd_12_26_9'][i] < 0 and df['macd_12_26_9'][i-1] > df['macds_12_26_9'][i-1]:
            MACD_Sell.append(np.nan)
            if position ==False:
                MACD_Buy.append(df['close'][i])
                position=True
            else:
                MACD_Buy.append(np.nan)
        elif abs(df['macd_12_26_9'][i]) > abs(df['macds_12_26_9'][i]) and df['macd_12_26_9'][i] > 0 and abs(df['macd_12_26_9'][i-1]) < abs(df['macds_12_26_9'][i-1]):
            MACD_Buy.append(np.nan)
            if position == True:
                MACD_Sell.append(df['close'][i])
                                    
                position=False
            else:
                MACD_Sell.append(np.nan)
        elif position == True and df['close'][i] > MACD_Sell[-1] * (1 - risk):
            MACD_Sell.append(df['close'][i])
            MACD_Buy.append(np.nan)
            position = False
        elif position == True and df['close'][i] < MACD_Buy[-1] * (1 - risk):
            MACD_Sell.append(df['close'][i])
            MACD_Buy.append(np.nan)
            position = False
        elif position == True and df['close'][i] < df['close'][i - 1] * (1 - risk):
            MACD_Sell.append(df["close"][i])
            MACD_Buy.append(np.nan)
            position = False
        else:
            MACD_Buy.append(np.nan)
            MACD_Sell.append(np.nan)


df['MACD_Buy_Signal_price'] = MACD_Buy
df['MACD_Sell_Signal_price'] = MACD_Sell
df.to_csv('tsla_stock.csv')
macd_plot = make_subplots(rows=3, cols=1)


macd_plot.append_trace(
    go.Scatter(
        x=df.index,
        y=df['close'],
        line=dict(color='#ff9900', width=1),
        name='close',
        legendgroup='1',
    ), row=1, col=1
)


macd_plot.append_trace(
    go.Candlestick(
        x=df.index,
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        increasing_line_color='green',
        decreasing_line_color='red',
        showlegend=False
    ), row=1, col=1
)


#  (%k)
macd_plot.append_trace(
    go.Scatter(
        x=df.index,
        y=df['macd_12_26_9'],
        line=dict(color='#ff9900', width=2),
        name='macd',
        legendgroup='2',
    ), row=2, col=1
)
# (%d)
macd_plot.append_trace(
    go.Scatter(
        x=df.index,
        y=df['macds_12_26_9'],
        line=dict(color='#000000', width=2),
 
        legendgroup='2',
        name='signal'
    ), row=2, col=1
)

design = np.where(df['macdh_12_26_9'] < 0, '#000', '#ff9900')

macd_plot.append_trace(
    go.Bar(
        x=df.index,
        y=df['macdh_12_26_9'],
        name='histogram',
        marker_color=design,
    ), row=2, col=1
)
macd_plot.append_trace(
    go.Scatter(
        mode = 'markers',
        x=df.index,
        y=df['MACD_Buy_Signal_price'],
        
        name='Buy Signal',
        legendgroup='1',
        marker = dict(color = 'black', size = 10),
    ), row=1, col=1
)

macd_plot.append_trace(
    go.Scatter(
        mode = 'markers',
        x=df.index,
        y=df['MACD_Sell_Signal_price'],
        
        name='Sell Signal',
        legendgroup='1',
        marker = dict(color = 'blue', size = 10),
    ), row=1, col=1
)

layout = go.Layout(
    plot_bgcolor='#efefef',
    font_family='Monospace',
    font_color='#000000',
    font_size=20,
    autosize=True,
    xaxis=dict(
        rangeslider=dict(
            visible=False
        )
    )
)

macd_plot.update_layout(layout)
macd_plot.show()

My Stock Trading Journey

Tuesday, October 18, 2022

Backtesting My planned MACD Trading Strategy

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