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In my last post, I played with a simple 2-day SMA strategy on TSLA and found surprisingly solid results.

But then came the inevitable comment:

Fair. So I did.

I add a basic slippage rate and trading fees — nothing extreme, just enough to mimic the real world.

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No Free Trades: Slippage and Fees in Practice

To reflect the realities of trading, I modified the SMA strategy to include basic cost factors:

Slippage, which accounts for imperfect execution prices

Commission, the fee per trade that eats into profits

These aren’t fancy models — just simple percentage — based adjustments. But even small frictions can change the picture fast.

Here’s the updated function:

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[brand] * (1 - slippage_rate)
    
    # Generate signal using ideal (close) price
    df['Signal'] = (df[brand] < 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[brand],label = brand ,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(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

Now that we have a version of the strategy that includes slippage and commission, it’s time to see how much impact they actually make.

I ran the same SMA backtest twice — once with trading costs, and once without — across different window size from 2 to 50.

Here’s the code for comparison:

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)

x = result.columns.to_list()
y1 = result.iloc[-1,:]

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

y2 = result.iloc[-1,:]

fig,ax = plt.subplots()
ax.plot(x,y1,label='Without Slippage&Commission')
ax.plot(x,y2,label='With Slippage&Commission')
ax.set_xlabel('Window size')
ax.set_ylabel('Final profit on sale')
ax.set_title('SMA Crossover Strategy: '+str(brand))
plt.grid()
plt.legend()

How much impact they actually make

Here’s what the results look like. The difference isn’t massive — but it’s there.

Despite the added friction, one thing didn’t change: The 2-day SMA still came out on top.

Not by a small margin, either. Even with slippage and fees, it delivered the strongest cumulative profit across all tested windows.

That was… unexpected.

Why Did it Still Work?

One possible reason is sheer trade frequency.

A 2-day SMA flips positions often-maybe too often in most markets. But with a volatile stock like TSLA, that hyper-reactive might actually help. It catches micro-trends, rides the short bursts, and exits before reversals hit too hard.

It’s not elegant. It’s not robust. But sometimes, brute force timing just work s— until it doesn’t.

Strategies like this aren7t bult to last forever. They ride a temporary edge — until the market adapts, volatility shifts, or friction overwhelms.

But that’s the point of backtesting.

For now, the 2-day SMA still stands. Next week? Who knows.