The Technical Indicators Explained

Before we examine the trading strategy, it’s essential to clarify the technical indicators incorporated in the code.

StochRSI:

This indicator merges the Stochastic Oscillator with the Relative Strength Index (RSI). It fluctuates between 0 and 1, signaling whether a stock is overbought or oversold. Values below 0.2 indicate oversold conditions, while values above 0.8 suggest overbought conditions.

df['STORSI'] = ta.momentum.stochrsi(close=df['close'], window=14, smooth1=3, smooth2=3, fillna=True)

Span A and Span B:

These are two critical components of the Ichimoku Cloud indicator. Span A is derived by averaging the Tenkan-sen and Kijun-sen lines and plotting the result 26 periods ahead. Span B is calculated by averaging the highest high and lowest low over the past 52 periods, also plotted 26 periods ahead.

Crafting the Trading Strategy

The trading strategy is fundamentally a trend-following method that initiates a purchase when the price surpasses both Span A and Span B, along with the StochRSI being under 0.8. Conversely, it sells when the price dips below either Span A or Span B, with the StochRSI exceeding 0.2.

df['SPANA'] = (ta.trend.ichimoku_a(high=df['high'], low=df['low'], window1=26, window2=52, visual=False, fillna=True))

df['SPANB'] = (ta.trend.ichimoku_b(high=df['high'], low=df['low'], window2=26, window3=52, visual=False, fillna=True))

df['EMA6'] = ((ta.trend.ema_indicator(close=df['close'], window=11, fillna=True))

The following loop processes the data to generate buy, sell, or hold signals:

signals = []

for i in range(1, len(df)-1):

if df.iloc[i]['close'] > df.iloc[i]['SPANA'] and df.iloc[i]['close'] > df.iloc[i]['SPANB'] and df.iloc[i]['STORSI'] < 0.8 and df.iloc[i]['close'] > df.iloc[i]['EMA6']:

signals.append(1) # Buy signal

elif (df.iloc[i]['close'] < df.iloc[i]['SPANA'] or df.iloc[i]['close'] < df.iloc[i]['SPANB']) and df.iloc[i]['STORSI'] > 0.2:

signals.append(-1) # Sell signal

else:

signals.append(0) # Hold signal

Once the signals are generated, the code incorporates them into the dataframe for further analysis.

Backtesting the Strategy

The backtesting phase is crucial as it assesses the trading strategy against historical data to estimate its effectiveness. The following code segment illustrates how trades are executed based on the generated signals:

investment = 1000

current_investment = 1000

is_invested = 0

for i in range(500, len(df)):

signal = df.iloc[i]['signal']

close = df.iloc[i]['close']

if signal == 1 and is_invested == 0: # Long entry

entry_point = close

quantity = (current_investment / close)

is_invested = 1

elif signal == -1 and is_invested == 1: # Long exit

profit = quantity * (close - entry_point)

current_investment += profit

is_invested = 0

# Additional conditions for short selling can be added here...

Through meticulous backtesting of this strategy, the investor managed to achieve a remarkable profit of $131,668. However, it’s essential to remember that previous performance does not guarantee future outcomes, and trading strategies inherently carry risks.

Important Reminder

It's crucial to fully comprehend the strategy before committing real capital and to always adhere to effective risk management practices.

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