What works (and what doesn't) in the stock market.
A synthesis of what 150 years of market history actually teach us. Drawn from 18 research papers, condensed into five principles and 24 production algos.
Most retail strategies start with a hunch. This study started with the data: roughly 150 years of market history, 18 research papers, and nine years spent compiling them into something tradeable. The conclusion is not a single magic signal. It is a small set of principles about risk, simplicity, and execution that survive across every market regime we tested, and a refusal to ship anything that violates them.
Nine years ago the question was simple, and a little uncomfortable: after a century and a half of price data, how much of what retail traders believe actually holds up? Not in a single backtest tuned to look good, but across booms, crashes, inflation, deflation, and the long flat stretches where nothing happens and most edges quietly die.
Most of it did not hold up. The indicator stacks, the “secret” setups, the systems that look perfect in a screenshot: run them on data they were not built on and they stop working. What was left was a short list. Boring, repeatable, and durable if you have the discipline to keep trading it.
The edges that survive are few, simple, and mostly about risk.
Everything that follows is built on that one observation. Five principles came out of the research. Each is traceable back to the papers that produced it, and each is enforced, not aspirational, in the algos we run.
The study
Read what 150 years of market data actually teaches us.
The latest version of the full study that ProRealAlgos is built upon. Then, when you are ready, the waitlist.
Research and backtested results. Past performance is not indicative of future results. Trading involves risk of loss.
The archive
The 18 papers behind the study.
The Best Days Myth
Why the 'miss the ten best days' argument quietly misleads, and what the full return distribution actually shows.
RP-02The Anatomy of a Bear Market
What bear markets across 150 years share, where they differ, and how they tend to end.
RP-03Inflation Regimes and Asset Returns
How stocks, bonds, and commodities behave across the inflation regimes markets keep cycling through.
RP-04The Moving-Average Filter, Re-examined
Whether a simple moving-average filter still adds anything once trading costs are counted.
RP-05Decomposing the Equity Risk Premium
Splitting long-run equity returns into the parts that persist and the parts that do not.
RP-06Dollar-Cost Averaging vs Lump Sum
When averaging in beats deploying all at once, and when it just quietly costs you return.
RP-07Trend Following Across a Century
How a simple trend rule holds up across 150 years and every regime in between.
RP-08Return Concentration: Do Stocks Beat Cash?
Most stocks underperform Treasury bills. A handful carry the entire market.
RP-09Carry as a Cross-Asset Strategy
Where the carry trade pays, where it turns on you, and what it really costs to hold.
RP-10The 60/40 Portfolio: A Verdict
Whether the classic balanced portfolio still earns its keep after the 2022 stress test.
RP-11Is Gold Actually a Hedge?
Testing gold's reputation against inflation, drawdowns, and currency debasement.
RP-12CAPE and the Limits of Valuation Timing
How much valuation signals like CAPE really tell you about the next decade of returns.
RP-13A Survivorship-Bias Audit
What re-running the classics with dead and delisted names does to their published results.
RP-14When Manual Overrides Underperform
What happens to returns every time the operator steps in to 'help' the system.
RP-15Position Sizing and Long-Run Return
How much you bet explains more of your equity curve than what you bet on.
RP-16Drawdowns and the Abandonment Point
Systems rarely die from low returns, they die from drawdowns nobody can sit through.
RP-17Knowledge Transfer and the Onboarding Problem
Why handing a working system to a new trader is where most of the risk actually lives.
RP-18Implementation Shortfall: The Hidden Cost
What slippage, latency, and hesitation quietly remove from a live strategy.
Five principles, distilled.
Risk management is the strategy.
Sizing and drawdown limits decide outcomes, not signals.
Complexity is usually compensation.
Fourteen indicators often hide that none of them work.
Automatic execution removes ego.
Take the operator's psychology out of the loop entirely.
Onboarding one trader at a time.
Personal handover, because capacity sets the pace.
If we don't run it, we don't sell it.
Every algo runs on the founder's own capital, live.