End-to-end Implementation with buy and sell signals

				
					import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import numpy as np

# Download historical data as dataframe
ticker = "ASML.AS"
df = yf.download(ticker, start='2020-01-01', end='2024-12-30')

def calculate_ADX(df, n):
    df['H-L'] = abs(df['High'] - df['Low'])
    df['H-PC'] = abs(df['High'] - df['Close'].shift(1))
    df['L-PC'] = abs(df['Low'] - df['Close'].shift(1))
    df['TR'] = df[['H-L', 'H-PC', 'L-PC']].max(axis=1)
    df['+DM'] = np.where((df['High'] > df['High'].shift(1)), df['High'] - df['High'].shift(1), 0)
    df['-DM'] = np.where((df['Low'] < df['Low'].shift(1)), df['Low'].shift(1) - df['Low'], 0)
    TRn = []
    DMpn = []
    DMnn = []
    TR = df['TR'].tolist()
    DMp = df['+DM'].tolist()
    DMn = df['-DM'].tolist()
    for i in range(len(df)):
        if i < n:
            TRn.append(np.NaN)
            DMpn.append(np.NaN)
            DMnn.append(np.NaN)
        elif i == n:
            TRn.append(df['TR'].rolling(n).sum().tolist()[n])
            DMpn.append(df['+DM'].rolling(n).sum().tolist()[n])
            DMnn.append(df['-DM'].rolling(n).sum().tolist()[n])
        elif i > n:
            TRn.append(TRn[i-1] - (TRn[i-1]/n) + TR[i])
            DMpn.append(DMpn[i-1] - (DMpn[i-1]/n) + DMp[i])
            DMnn.append(DMnn[i-1] - (DMnn[i-1]/n) + DMn[i])
    df['TRn'] = np.array(TRn)
    df['+DMn'] = np.array(DMpn)
    df['-DMn'] = np.array(DMnn)
    df['+DI'] = 100 * (df['+DMn']/df['TRn'])
    df['-DI'] = 100 * (df['-DMn']/df['TRn'])
    df['DIdiff'] = abs(df['+DI'] - df['-DI'])
    df['DIsum'] = df['+DI'] + df['-DI']
    df['DX'] = 100 * (df['DIdiff']/df['DIsum'])
    ADX = []
    DX = df['DX'].tolist()
    for j in range(len(df)):
        if j < 2*n-1:
            ADX.append(np.NaN)
        elif j == 2*n-1:
            ADX.append(df['DX'][j-n+1:j+1].mean())
        elif j > 2*n-1:
            ADX.append(((n-1)*ADX[j-1] + DX[j])/n)
    df['ADX'] = np.array(ADX)
    return df['ADX']

calculate_ADX(df, 14)

# Generate signals
df['signal'] = 0
df.loc[(df['ADX']>25) & (df['+DI']>df['-DI']), 'signal'] = 1
df.loc[(df['ADX']>25) & (df['-DI']>df['+DI']), 'signal'] = -1

# Identify the points where the signals change from 0 to 1 or -1
#df['buy_signal'] = ((df['signal'] == 1) & (df['signal'].shift(1) == 0))
#df['sell_signal'] = ((df['signal'] == -1) & (df['signal'].shift(1) == 0))

# Plotting
fig, (ax1, ax2) = plt.subplots(2, figsize=(30, 14))

# Plot the closing price
df['Close'].plot(ax=ax1)
ax1.set_ylabel('Price')

# Plot the entry and exit points
#ax1.plot(df[df['buy_signal']].index, df['Close'][df['buy_signal']], '^', markersize=10, color='g', label='buy')
#ax1.plot(df[df['sell_signal']].index, df['Close'][df['sell_signal']], 'v', markersize=10, color='r', label='sell')
ax1.legend()

# Plot the ADX, +DI, -DI
df['ADX'].plot(ax=ax2, color='black', label='ADX')
df['+DI'].plot(ax=ax2, color='green', label='+DI')
df['-DI'].plot(ax=ax2, color='red', label='-DI')
ax2.set_ylabel('ADX')
ax2.legend()

# Show the plot
plt.show()

'''
The ADX line shows the strength of the trend. Generally, an ADX above 25 is considered to indicate a strong trend, while an ADX below 20 suggests a weak or non-trending market.
The +DI and -DI lines can help identify the direction of the trend. When +DI is above -DI, it suggests an upward trend, and when -DI is above +DI, it suggests a downward trend.
The trading signals indicate potential points to enter or exit the market. A signal of 1 suggests a potential buying opportunity (entering a long position or closing a short position), 
while a signal of -1 suggests a potential selling opportunity (entering a short position or closing a long position). 
These signals are based on a very basic strategy and may not always be accurate or profitable.
'''