Every strategy on SignalStrike is rules-based, backtestable, and auditable. Here's exactly how it works.
One of the most well-documented anomalies in financial markets.
Momentum is the empirical observation that stocks which have performed well over a recent period tend to continue performing well, and stocks that have performed poorly tend to continue underperforming. It's not a theory — it's a measured, repeatable pattern.
Jegadeesh and Titman first documented this effect in their landmark 1993 paper, showing that buying recent winners and selling recent losers generated significant excess returns over 3- to 12-month holding periods. Their findings have been replicated across decades, geographies, and asset classes.
Momentum is now recognized as one of the core factors in the Fama-French multi-factor model, alongside value, size, profitability, and investment. With 30+ years of academic research and institutional adoption behind it, momentum is not a niche strategy — it's a foundational building block of modern portfolio construction.
SignalStrike turns this academic foundation into a practical, configurable tool. You choose the parameters. You run the backtests. You see exactly how the algorithm selects stocks.
The tendency of assets with strong recent performance to continue outperforming over intermediate time horizons (typically 3-12 months).
Excess return above a benchmark (e.g., S&P 500). Positive alpha means the strategy outperformed; negative means it underperformed.
Risk-adjusted return metric. Measures return per unit of volatility. Higher is better. Above 1.0 is generally considered strong.
The peak-to-trough decline during a specific period. Maximum drawdown is the worst such decline — the number that keeps investors up at night.
The periodic process of selling positions that no longer rank highly and buying new momentum leaders. SignalStrike rebalances monthly.
A systematic, rules-based process applied to every stock, every day.
Every day, SignalStrike screens 3,000+ stocks across 20+ indices — S&P 500, Nasdaq 100, Russell 2000, sector ETFs, and more. Every stock in the universe is evaluated.
Each stock receives a composite momentum score based on configurable lookback periods — 30-day, 60-day, 90-day, 6-month, or 12-month. You choose the period that fits your strategy.
Stocks are ranked by momentum score and filtered through your selected parameters. The top performers are selected for your basket based on your chosen selection mode.
Every basket includes configurable risk controls designed for all market conditions.
Sharpe-like scoring penalizes high-volatility momentum. A stock up 50% with extreme swings ranks lower than one up 40% with smooth returns. Rewards consistency, not just magnitude.
Restrict baskets to mega-cap, large-cap, or mid-cap stocks. Controls liquidity risk and concentration in speculative names. Configurable per basket.
Overbought protection. Screens out stocks with RSI above configurable thresholds (default: 70). Prevents buying into exhausted rallies at unsustainable levels.
Trend confirmation via MACD crossover. Ensures momentum stocks also have positive trend direction, filtering out names in momentum reversal.
Cap exposure to any single sector or industry. Prevents momentum baskets from becoming concentrated bets on one corner of the market.
Bond flight, cash conversion, war scenario allocation, and short momentum — designed for bear markets, crises, and regime shifts. Adapt your strategy without rebuilding it.
The same algorithm runs live and in backtests. No special sauce. No data snooping.
SignalStrike uses the exact same algorithm for backtesting and live trading. There is no separate "backtest engine" with different logic. The code that selects stocks for your portfolio today is the same code that runs through historical data.
Historical prices are sourced from Tiingo, using split-adjusted closing prices (adjClose). Rebalancing occurs monthly at the closing price on the rebalance date — no intraday optimization or look-ahead bias.
Every parameter you configure is applied identically in backtests and live mode: momentum period, selection mode, weighting, filters, and risk controls. You can run any backtest yourself and verify the results.
We believe transparency builds trust. If you can't reproduce our results, we haven't earned your confidence.
Important: Backtest Limitations
Index Membership: Backtests use point-in-time index membership: a stock is ranked on a historical date only if it was a member of the selected index on that date. Coverage is the S&P 500 from November 2005 and the NASDAQ 100 and Dow Jones Industrial Average from July 2023; other indices, and earlier dates, use current membership. Former index members are included as candidates where their price history is available (comprehensive from January 2016, partial before that), so results for earlier periods may still reflect some survivorship bias.
Total Return, Including Dividends: Strategy and benchmark returns are shown on a total-return basis: cash dividends paid on holdings while they were held are credited to the portfolio and reinvested at the next rebalance, and the benchmark is dividend-adjusted. Where a symbol has no dividend data for a period, nothing is credited for that period.
Zero Transaction Costs: Trades are modeled at the same closing price used for ranking, with zero commissions, zero slippage and fractional shares. Real-world execution incurs costs and price impact that reduce returns. Real-world trading incurs costs that reduce returns — especially for frequent rebalancing or less liquid positions.
Split-Adjusted Prices, Dividends Credited Separately: Price data from Tiingo is split-adjusted but not dividend-adjusted, so stock splits are reflected in the price series and dividends are not. Dividends are credited to the portfolio separately, as cash on the ex-date, rather than being folded into the price used for momentum ranking.
Execution Assumptions: Trades are modeled at the same closing price used for ranking that day, and backtests assume fractional shares are available so every dollar of a target allocation is fully deployed. Real-world execution occurs at the market price when the order fills, and most brokerages only support whole-share trading, so actual fills and position sizes will differ.
Hypothetical Results: Backtested performance is hypothetical and does not represent actual trading. Past performance — whether backtested or live — is not indicative of future results. All investing involves risk, including the loss of principal.
Transparent sourcing. No proprietary black boxes.
Primary price data is split-adjusted but not dividend-adjusted; dividends are credited to portfolio value separately as cash. See the Backtesting Integrity section for implications.
See the methodology in action. Configure a basket, test it against real data, and review the results.