Overview
This strategy is intentionally simple and rules‑based:
- Trend filter: SMA(50) must be above SMA(200).
- Entry: Price above SMA(50) and RSI(2) below 40 during short pullbacks.
- Exit: Close below SMA(50) minus 0.5 × ATR(14) or when the global trend (SPY close < SMA200) flips.
- Risk controls: 20% stop loss and commission sensitivity checks.
“We use a long-term moving average with a period of 200 and a short-term one with a period of 50. If the 50-day average is above the 200-day average, it means the market is in an uptrend.”
Why this works
- Trend confirmation first: The system only trades when the market shows a clear uptrend, reducing false breakouts.
- Buy the dip: Waiting for short V‑shaped pullbacks (RSI in lower half) improves entry quality.
- Ranking and diversification: Selecting up to 10 names from the S&P 100 by 20‑day ROC concentrates on recent leaders while spreading idiosyncratic risk.
- Global filter: Adding SPY’s SMA200 as a market filter reduced drawdowns significantly in the demo.
Backtest highlights
- Single‑ETF test showed modest edge (CAGR ~4%, drawdown ~29%).
- S&P 100 basket with ranking produced higher CAGR but initially large drawdown; adding the SPY trend filter and exit rule improved CAGR and cut drawdown from ~63% to ~35% then to ~22% after RSI tweak.
- Normalized by drawdown or holding period, the strategy outperformed buy‑and‑hold by multiples in the demo.
Practical checklist to implement
- Data: Daily prices for S&P 100 constituents; start date as needed.
- Indicators: SMA50, SMA200, ATR14, RSI2, ROC20.
- Portfolio rules: Max 10 positions; rank by ROC20; position sizing 100% equity split across positions or fixed percent.
- Exits: SMA50 − 0.5×ATR14 or SPY below SMA200; optional 20% stop loss.
- Robustness: Test commissions (0.1% used), slippage, and out‑of‑sample periods.
Limitations and next steps
- Survivorship bias: Use historical constituent lists to avoid bias.
- Execution: Real slippage and liquidity constraints matter for large sizes.
- Optimization risk: Keep parameters conservative; prefer robustness over curve‑fitting.
- Tools: The demo used StrategyQuant X tools; for brokers or deposit bonus promotions reference IC Markets where applicable.
Conclusion
A compact momentum approach — trend confirmation, disciplined dip entries, ranking, and simple risk controls — can materially improve returns versus passive exposure when properly tested and risk‑managed. The demo’s results are compelling but require careful implementation and ongoing monitoring.
