Strategy Report

SMA 200 Trend Following (GOLD)

Buy when price crosses above the 200-day SMA, exit when it crosses below. The canonical long-term trend filter applied to gold — a market known for strong secular trends.

trend-followingcommoditieslong-term

Strategy Logic & Backtest Setup

Buy when price crosses above SMA(200). Sell when price crosses below SMA(200). 5% stop loss.

63

Robustness

Moderate
304 scenarios · GOLD

Robustness Score: 62/100. Moderate robustness with notable sensitivities. Typical return +0.0%, worst-case -9.1% (5th percentile).

Typical Return
+0.0%
median across cases
Worst Case
-9.1%
5th percentile
Win Rate
40%
4/10 cases

How does this strategy behave in different market regimes?

Model results from 304 simulation runs across curated historical market phases and synthetic stress tests, aggregated by market regime.

Values are model estimates: average and range (±1 standard deviation) from Monte Carlo variations across historical market phases and synthetic stress tests. Not a prediction of future performance, not investment advice.

Rising Market

Stocks & indices climb over weeks or monthslike Bull Run 2017 or Tech Rally 2021.

Average+6.5%
Range-5.8%+18.7%
Sample152 cases

Sideways Market

Market drifts directionless inside a rangelike SPY 2015 or Range 2011–2012.

Average-3.1%
Range-3.1%-3.1%
Sample2 cases · 2 subtypes

Calm Market

Low volatility, muted price actionlike mid-2017 or pre-Lehman 2007.

Average+3.0%
Range-3.2%+9.1%
Sample50 cases

High Volatility

Large daily swings, vol spikeslike the February 2018 vol shock.

Average+3.2%
Range-9.3%+15.7%
Sample101 cases

Falling Market

Markets decline over an extended periodlike Dotcom Bust 2001 or 2022 Bear Market.

Average-1.1%
Range-4.2%+1.9%
Sample201 cases · 2 subtypes

Market Crash

Sudden sharp drawdowns, liquidity stresslike Lehman 2008 or COVID Crash 2020.

Average+10.3%
Range-6.2%+26.8%
Sample52 cases · 2 subtypes

Aggregation note: Cases can contribute to multiple regimes — a crash counts as both "Market Crash" and "High Volatility", for example. Range combines within-subtype and between-subtype spread. Total 558 case contributions across 9 failure modes.

Weakest spot · Whipsaw

Your strategy returned -3.1% on average across sideways markets with false signals. Require signal confirmation (e.g. close above the level for N bars) to filter noise.

Model-based scenario simulation. All values are produced by computational market models — curated historical market phases and synthetic stress tests with Monte Carlo variations. They describe how the strategy behaves in the modelled scenarios, not future performance in live markets.

Not investment advice, not financial analysis under § 34b WpHG (German Securities Trading Act), not a recommendation to buy or sell. Past or simulated performance is not a reliable indicator of future results.

These results are based on model-driven simulations under simplified assumptions. They do not constitute a forecast, recommendation, or financial advice. Real market outcomes may differ significantly.

Case Studies

Strategy performance across curated market episodes — real historical periods plus synthetic stress scenarios. Each case is chosen to test a distinct failure mode of trading strategies.

Computing case studies...