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In my previous article, I tested a simple 2-day SMA crossover strategy on Tesla stock. Even after factoring in slippage and commissions, the strategy still managed to stay profitable — surprisingly so.

But was that a fluke unique to TSLA’s chaotic price action?

This time, I applied the same setup to a few other major tech stock to find out.

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So here’s the code used for this analysis

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

def sma_strategy_slippage_commission(df_org,window,slippage_rate,commission_rate,draw = True):
    df = df_org.copy()
    # Calculate SMA based on closing price
    df['SMA'] = df.rolling(window=window).mean().fillna(0)

    # Calculate execution price with slippage (assume buying slightly worse than close)
    df['ExecPrice'] = df[ticker] * (1 - slippage_rate)
    
    # Generate signal using ideal (close) price
    df['Signal'] = (df[ticker] < df['SMA']).astype(int)
    df['Position'] = df['Signal'].diff()

    # Buy and sell points using slippage-adjusted execution price
    df['Buy'] = (df['Position'] == 1).astype(int)*df['ExecPrice']
    df['Sell'] = (df['Position'] == -1).astype(int)*df['ExecPrice']
    
    # If there is a final unmatched Buy, we force-close it at the last available price
    if (df['Buy'] - df['Sell'] ).sum() > 0:
        df['Sell'].iat[-1] = df['ExecPrice'].iat[-1]

    # Compute commissions for each trade
    df['Buy_commission'] = df['Buy'] * commission_rate
    df['Sell_commission'] = df['Sell'] * commission_rate

    # Calculate profit net of slippage and commissions
    df['Profit'] = (df['Sell'] - df['Buy'] - df['Buy_commission'] - df['Sell_commission']).cumsum()
    df['Profit on sale'] = ((df['Sell'] > 0).astype(int)*df['Profit']).cumsum()

    if draw is True:
        fig,axs = plt.subplots(2,1,figsize=(14,8))
        ax = axs[0]
        ax.plot(df.index,df[ticker],label = ticker ,color='black')
        ax.plot(df.index[window:],df['SMA'][window:],label = 'SMA' ,color='grey')
        ax.scatter(df.index,(df['Buy']).replace(0,np.nan),label='Buy',color='blue')
        ax.scatter(df.index,(df['Sell']).replace(0,np.nan),label='Sell',color='red')
        ax.label_outer()
        ax.set_title('SMA Crossover Strategy: '+str(ticker))
        ax.set_ylabel('Price')
        ax.grid()
        ax.legend()
        
        ax = axs[1]
        ax.plot(df.index,df['Profit on sale'])
        ax.set_ylabel('Pofit')
        ax.set_xlabel('Date')
        ax.grid()
    
    return df

Quick Comparison Across Stocks

I ran the strategy across five major tickers: TSLA, AAPL, MSFT, and META.

Here’s a simple bar chart comparing their final profits (after accounting for slippage and commissions):

tickers = ['TSLA','AAPL','NVDA','MSFT','META']
window = 2
slippage_rate = 0.001
commission_rate = 0.001

fig, ax = plt.subplots()
ax.axhline(0, color='black', linestyle='--', linewidth=1)
ax.set_ylabel('Final profit on sale (USD)')
color = 'lightgrey'
prof_df = pd.DataFrame(index=tickers)
ys = []
for ticker in tickers:
    df_org = yf.download(ticker, start="2024-04-01", end="2025-06-07")['Close']
    df = sma_strategy_slippage_commission(df_org,window,slippage_rate,commission_rate,False)
    y = df['Profit on sale'].iat[-1]
    ys.append(y)
    color = 'lightgrey' if 0 <= y else 'darkred'
    ax.bar(ticker,y,color=color)
prof_df['Final profit on sale'] = ys

The differences were bigger than I expected. Here’s how each stock performed under the same 2-day SMA strategy:

Why Did These Two Lose Money?

While TSLA, AAPL, and NVDA stayed in the green, MSFT and META ended up losing money under the same conditions.

Here’ a quick dive into why.

To investigate further, I plotted each stock’s final profit against its 5-day volatility.

The idea: maybe the more chaotic stocks just happened to benefit from the strategy.

tickers = ['TSLA','AAPL','NVDA','MSFT','META']
start_date = "2024-04-01"
end_date = "2025-06-07"

volatility = {}

for ticker in tickers:
    df = yf.download(ticker, start=start_date, end=end_date)['Close']
    returns = df.pct_change().dropna()
    vol = returns.rolling(window=2).std().mean()
    volatility[ticker] = vol[ticker]

vol_df = pd.DataFrame.from_dict(volatility, orient='index', columns=['5-day Volatility'])

fig, ax = plt.subplots()
ax.axhline(0, color='black', linestyle='--', linewidth=1)
ax.set_xlabel('5-day Volatility (%)')
ax.set_ylabel('Final profit on sale (USD)')
for ticker in prof_df.index:
    x = vol_df['5-day Volatility'][ticker]*100
    y = prof_df['Final profit on sale'][ticker]
    ax.scatter(x,y,color='grey')
    ax.text(x,y,ticker)

Here’s what that looks like visually: each dot represents one tech stock, showing how volatility might relate to strategy performance.

What This Tells Us

Despite the simplicity of the 2-day SMA, it manages to capture meaningful signals — especially when paired with volatile stocks.

It doesn’t work across the board, as seen with MSFT and META, but when the market’s noisy, reacting quickly seems to pay off.

In that sense, this isn’t just a strategy — it’s a vibe check.

If the market’s calm, a reactive system like this one might just fizzle out.

But in chaos? It thrives.

Maybe that’s deeper lesson:

Volatility isn’t a bug — it’s the feature this strategy is built to exploit.