Effective Lesser-Known Trading Indicators Implemented - Choppiness, Disparity, Awesome, Three Way, RVI and More

				
					import matplotlib.lines as mlines
import yfinance as yf
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
import matplotlib.colors as mcolors
import numpy as np
import matplotlib.ticker as ticker
import pandas as pd

# Set the start and end dates
start_date = '2020-01-01'
end_date = '2024-03-26'
symbol = 'UNA.AS'

def moving_average_strategy(df, ma1, ma2, ma3):
    # Calculate the moving averages
    ma_short = df['Close'].rolling(window=ma1).mean()
    ma_mid = df['Close'].rolling(window=ma2).mean()
    ma_long = df['Close'].rolling(window=ma3).mean()

    # Find where the moving averages cross
    cross_buy = ((ma_short > ma_long) & (ma_mid > ma_long)) & ((ma_short.shift() < ma_long.shift()) | (ma_mid.shift() < ma_long.shift()))
    cross_sell = (ma_short < ma_mid) | (ma_short < ma_long)

    first_buy_signal = False
    signals = pd.DataFrame(columns=['Type', 'Date', 'Price'])

    for date, row in df.iterrows():
        if cross_buy.loc[date] and not first_buy_signal:
            first_buy_signal = True
            new_signal = pd.DataFrame({'Type': ['Buy'], 'Date': [date], 'Price': [row['Low']]})
            signals = pd.concat([signals, new_signal], ignore_index=True)
        elif cross_sell.loc[date] and first_buy_signal:
            first_buy_signal = False
            new_signal = pd.DataFrame({'Type': ['Sell'], 'Date': [date], 'Price': [row['High']]})
            signals = pd.concat([signals, new_signal], ignore_index=True)

    signals.reset_index(drop=True, inplace=True)

        # Calculate the returns for each trade
    signals['Returns'] = np.nan

    for i in range(0, len(signals) - 1, 2):
        buy_price = signals.iloc[i]['Price']
        sell_price = signals.iloc[i + 1]['Price']
        signals.iloc[i + 1, signals.columns.get_loc('Returns')] = sell_price - buy_price

    # Calculate the metrics
    total_net_profit = signals['Returns'].sum()
    losing_trade_sum = abs(signals[signals['Returns'] < 0]['Returns'].sum())
    profit_factor = signals[signals['Returns'] > 0]['Returns'].sum() / losing_trade_sum if losing_trade_sum != 0 else np.inf
    percent_profitable = len(signals[signals['Returns'] > 0]) / (len(signals) / 2) * 100
    average_trade_net_profit = signals['Returns'].mean()
    drawdown = (signals['Price'].cummax() - signals['Price']).max()

    return total_net_profit, average_trade_net_profit

# Get the data from Yahoo Finance using yfinance library
stock_ohlc = yf.Ticker(symbol)
df = stock_ohlc.history(start=start_date, end=end_date)

# Define the range of values for the moving average parameters
ma1_range = range(3, 21)
ma2_range = range(3, 21)
ma3_range = range(3, 21)

# Initialize the best parameters and their associated metrics
best_params = (0, 0, 0)
best_total_net_profit = -np.inf
best_net_profit_per_trade = -np.inf

# Perform a grid search to find the best parameters
for ma1 in ma1_range:
    for ma2 in ma2_range:
        for ma3 in ma3_range:
            if ma1 != ma2 and ma1 != ma3 and ma2 != ma3:
                total_net_profit, net_profit_per_trade = moving_average_strategy(df, ma1, ma2, ma3)

                if total_net_profit > best_total_net_profit and net_profit_per_trade > best_net_profit_per_trade:
                    best_params = (ma1, ma2, ma3)
                    best_total_net_profit = total_net_profit
                    best_net_profit_per_trade = net_profit_per_trade

print("Best Parameters: MA1 =", best_params[0], "MA2 =", best_params[1], "MA3 =", best_params[2])
print("Best Total Net Profit:", best_total_net_profit)
print("Best Net Profit Per Trade:", best_net_profit_per_trade)

def major_date_formatter(x, pos=None):
    dt = mdates.num2date(x)
    if dt.day == 1:
        return f'{dt.strftime("%b")} {dt.year}'
    return ''

def minor_date_formatter(x, pos=None):
    dt = mdates.num2date(x)
    if dt.day == 1:
        return f'{dt.day}\n\n{dt.strftime("%b")} {dt.year}' if dt.month == 1 else f'{dt.day}\n\n{dt.strftime("%b")}'
    return f'{dt.day}'

# Get the data from Yahoo Finance using yfinance library
stock_ohlc = yf.Ticker(symbol)
df = stock_ohlc.history(start=start_date, end=end_date)

ma_1_window = 3
ma_2_window = 6
ma_3_window = 5

# Calculate the moving averages
ma_1 = df['Close'].rolling(window=ma_1_window).mean()
ma_2 = df['Close'].rolling(window=ma_2_window).mean()
ma_3 = df['Close'].rolling(window=ma_3_window).mean()

# Find where the moving averages cross
cross_buy = ((ma_1 > ma_3) & (ma_2 > ma_3)) & ((ma_1.shift() < ma_3.shift()) | (ma_2.shift() < ma_3.shift()))
cross_sell = (ma_1 < ma_2) | (ma_1 < ma_3)

# Convert the date to the matplotlib date format
df['Date'] = mdates.date2num(df.index)

# Create the plot
fig, ax = plt.subplots(figsize=(15, 8))

# Plot the moving averages
ax.plot(df.index, ma_1, color='tab:blue', label='{0}-day MA'.format(ma_1_window), linestyle='--')
ax.plot(df.index, ma_2, color='tab:orange', label='{0}-day MA'.format(ma_2_window), linestyle='--')
ax.plot(df.index, ma_3, color='tab:green', label='{0}-day MA'.format(ma_3_window), linestyle='--')

# Plot the candlesticks
candlestick_ohlc(ax, df[['Date', 'Open', 'High', 'Low', 'Close']].values, width=0.6, colorup='green', colordown='red')

# Create markers for buy and sell signals
buy_marker = mlines.Line2D([], [], color='blue', marker='^', linestyle='None', markersize=10, label='Buy')
sell_marker = mlines.Line2D([], [], color='red', marker='v', linestyle='None', markersize=10, label='Sell')

# Initialize buy and sell signal flags
first_buy_signal = False
buy_dates, buy_prices, sell_dates, sell_prices = [], [], [], []

# Record prices and dates for buy and sell signals
for date, row in df.iterrows():
    if cross_buy.loc[date] and not first_buy_signal:
        first_buy_signal = True
        buy_dates.append(date)
        buy_prices.append(row['Low'])
    elif cross_sell.loc[date] and first_buy_signal:
        first_buy_signal = False
        sell_dates.append(date)
        sell_prices.append(row['High'])

# Plot the buy and sell markers
ax.plot(buy_dates, buy_prices, '^', markersize=7, color='blue', label='Buy')
ax.plot(sell_dates, sell_prices, 'v', markersize=7, color='red', label='Sell')

# Add the legend and title
ax.legend(handles=[buy_marker, sell_marker])
ax.set_title(symbol + ' Stock Price Chart with Buy and Sell markers')

# Set y-axis label
ax.set_ylabel('Price')

plt.show()