Understanding SMA Crossover Strategy & Backtesting

What is a Simple Moving Average (SMA)?

An SMA is a technical indicator that calculates the average price of an asset over a specific number of past periods. For example, a 20-day SMA is the average closing price of the asset over the last 20 trading days. SMAs smooth out price data to help identify the direction of the trend.

What is an SMA Crossover Strategy?

This is a common trend-following strategy that uses two SMAs: a shorter-period SMA and a longer-period SMA.

• Buy Signal (e.g., "Golden Cross"): Typically generated when the shorter-period SMA crosses *above* the longer-period SMA. This may indicate the start of an uptrend. This tool simulates entering a LONG position.
• Sell Signal (e.g., "Death Cross"): Typically generated when the shorter-period SMA crosses *below* the longer-period SMA. This may indicate the start of a downtrend. This tool simulates Exiting the LONG position (going flat).

This tool tests a long-only strategy based on these crossover signals.

What is Backtesting?

Backtesting is the process of applying a trading strategy or analytical method to historical data to see how it would have performed in the past. The goal is to assess the strategy's viability before risking real capital.

Interpreting Performance Metrics (Calculated by this Tool):

• Total Net Profit/Loss: The overall financial outcome of the strategy over the backtest period, after commissions.
• Total Net Profit/Loss (%): If initial capital is provided, this is the total P/L as a percentage of that capital.
• Number of Trades: Total buy/sell transactions executed.
• Win Rate: Percentage of trades that were profitable. (Winning Trades / Total Trades) * 100.
• Average Win/Loss: The average amount gained on winning trades versus lost on losing trades.
• Profit Factor: Gross Profit (sum of all winning trades) divided by Gross Loss (sum of absolute values of all losing trades). A value greater than 1 indicates profitability.
• Maximum Drawdown: The largest peak-to-trough percentage decline in the portfolio's equity curve during the backtest. This is a key measure of downside risk.

Critical Limitations & Pitfalls of Simple Backtesting:

• Past performance is not indicative of future results. Market conditions change.
• Overfitting (Curve Fitting): Designing a strategy or choosing parameters that fit the historical data perfectly but fail on new, unseen data. This tool uses fixed SMA periods input by the user, but repeated testing with different parameters on the same data can lead to overfitting.
• Data Quality & Survivorship Bias: The accuracy of the backtest depends on clean historical data. Using data that only includes "surviving" assets (and excludes those that failed) can skew results positively. This tool relies on user-provided data.
• Look-Ahead Bias: Using information in the strategy logic that would not have been available at the actual time of decision-making during the historical period. (This simple SMA Crossover is generally safe from this if implemented correctly).
• Ignoring Transaction Costs & Slippage: This tool allows for basic commission input. However, real-world slippage (the difference between the expected trade price and the actual execution price) is not modeled here and can significantly impact profitability, especially for frequently traded strategies or illiquid assets.
• Ignoring Market Impact: Large trades can themselves move market prices, an effect not captured in simple backtests. (Less relevant for typical retail "shares per trade".)
• Changing Market Regimes: A strategy that worked well in one type of market (e.g., trending) might perform poorly in another (e.g., ranging).

This Tool's Purpose: This "Simple Moving Average (SMA) Crossover Strategy Tester" is for educational exploration using **your manually inputted price data**. It demonstrates how a basic strategy might have performed historically under idealized conditions (plus basic commissions). **It is NOT investment advice, and the results are hypothetical.** Always be highly critical of backtest results and understand their limitations before considering any strategy for live trading.