SMA vs. Tesla Chaos: Backtesting Through the Hype

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With Tesla stock swinging wildly after Trump’s latest jabs at Elon Musk, I couldn’t help but wonder.

Would a simple moving average (SMA) strategy survive this kind of hype-fueled volatility?

So I ran a simple backtest using TSLA’s daily prices from the past year. No machine learning, no fancy modeling — just a classical SMA crossover strategy.

This isn’t about predicting the market or proving anything definitive. It’s about seeing how a plain old rule behaves when the noise gets loud.

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Wrapping the SMA Strategy into a Simple Function

Rather than rewriting the same logic over and over, I wrapped the SMA strategy I wrote in the previous article into a function. This also makes it easier to experiment with things like window size and stock tickers.

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

def sma_strategy(df_org,window,draw = True):
    df = df_org.copy()
    df['SMA'] = df.rolling(window=window).mean().fillna(0)
    df['Signal'] = (df[brand] < df['SMA']).astype(int)
    df['Position'] = df['Signal'].diff()
    df['Buy'] = (df['Position'] == 1).astype(int)
    df['Sell'] = (df['Position'] == -1).astype(int)
    
    # 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] = 1
    
    df['Profit'] = (df['Sell']*df[brand] - df['Buy']*df[brand]).cumsum()
    df['Profit on sale'] = (df['Profit']*df['Sell']).cumsum()

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

Here’s how I used the function to test SMA on Tesla (TSLA) over the past year.

brand = 'TSLA'
df_org = yf.download(brand, start="2024-04-01", end="2025-06-07")['Close']
sma_strategy(df_org,20)

Here’s the cumulative profit using a simple 20-day SMA on TSLA. I wasn’t expecting much from such a basic rule, but it actually ended up in the green.

Exploring Different SMA windows

After seeing that the 20-day SMA surprisingly produced a positive result, I wanted to see how the strategy would perform across a range of window sizes.

The following loop runs the same logic for window size from 2 to 50, and collects the final cumulative profit from each run.

dfs = []
for window in range(2,51):
    df = sma_strategy(df_org,window,False)
    dfs.append(df['Profit on sale'].to_frame(name=window))
result = pd.concat(dfs,axis=1)

result.iloc[-1,:].plot(xlabel='Window size',ylabel='Final profit on sale', title = 'SMA Crossover Strategy: '+str(brand), grid = True)

Turns out, the choice of window size makes a huge difference. Some windows barely stay profitable — others amplify the trend just enough to catch solid gains.

A Closer Look at Window size = 2

Among all the window size I tested, the 2-day SMA turned out to be the most profitable — by a large margin. To dig deeper, I plotted the buy/sell points along with the cumulative profit timeline. Let’s see how this ultra-short-term strategy behaved over time.

brand = 'TSLA'
df_org = yf.download(brand, start="2024-04-01", end="2025-06-07")['Close']
sma_strategy(df_org,2)

Can You Strategize Around Chaos?

The 2-day SMA looks like a winner — for now. But when stock movements hinge on late-night tweets and unpredictable emotions, maybe “strategy” is just a comforting myth.

In markets ruled by chaos, not planning might be the ultimate plan.