TechAnalyzer ProStock Market Trading Strategies: The Complete Guide, With Measured Numbers
A trading strategy is three rules written in advance: when to enter, when to exit, and how much to buy. Anything without these three rules isn't a strategy, it's a feeling. And a strategy that hasn't been backtested on historical data with costs is a hypothesis, not a method. On this page we've compiled the main families of trading strategies, and what happened when we ran the famous ones on the exact same data, against the index.
One piece of advice before anything else: none of the methods here guarantee profit, and anyone who sells you certainty is selling you a fantasy. What you can get from a good strategy is a small edge, one that repeats itself, with losses that stay small. That's the whole story, and it's enough.
Three Families, Three Different Beliefs About the Market
Every strategy assumes something about price behavior. It's worth knowing what you're assuming before you buy.
- Trend Following: What's rising tends to keep rising. Buy breakouts and hold until the trend breaks. Examples: Turtle Trading System, Moving Average CrossoverLow win rate, large and rare profits, long losing streaks.
- Mean Reversion: What's stretched too far snaps back. Buy sharp dips within an uptrend and sell quickly. Examples: RSI2, StochasticHigh win rate, small profits, and a single loss that can wipe out a month.
- Cross-Sectional Momentum: Not "is the stock rising" but "is it rising more than the others." Buy the leaders and rotate monthly. Example: 12-1 MomentumWorks for years, then collapses all at once.
- Price Structure Swing Trading: Breakout and Retest of a level, with a stop below the structure and a target at the next level. This is the family that TechAnalyzer Pro's engine runs, which is why it has its own page here with the track record.
- Day Trading: The same ideas in minutes instead of days, for example Opening Range BreakoutThin edge, large costs, and a full-time profession.
The Tournament: What Happened When We Ran Them on the Same Data
Instead of debating which is "better," we ran the classic rules of each method on the same universe and the same years, against two benchmark indices: buy and hold S&P 500, and the same buy with 200-Average TimingThe measurement: 184 American stocks and ETFs, daily candles, May 2023 to September 2026 (840 trading days), execution at the open after the signal, cost of 0.05% per side, long only and no leverage. The universe is today's and not 2023's — stocks that collapsed and disappeared are not included, and this biases every result upward. The market rose throughout most of the period.
| Strategy | Total Return | Annual Return | Maximum Drawdown | Trades | Win Rate |
|---|---|---|---|---|---|
| S&P 500 Buy and Hold | +95% | 22.1% | -18.8% | — | — |
| S&P 500 with 200 average timing | +74% | 18.1% | -10.9% | 4 | — |
| Turtle Trading System (Breakout of 55 days) | +98% | 22.8% | -15.9% | 401 | 34% |
| 20/50 moving average crossover | +110% | 24.8% | -17.8% | 129 | 44% |
| Golden cross 50/200 | +70% | 17.3% | -19.3% | 28 | 46% |
| 12-1 Momentum | +188% | 37.3% | -32.3% | 344 | 53% |
| RSI2 (mean reversion) | +14% | 4.1% | -25.6% | 2,314 | 62% |
| Stochastic 14,3,3 | +19% | 5.4% | -19.1% | 441 | 57% |
How to read the table without getting carried away
- The benchmark is the index. In a market that rose 95%, a method that earned 70% lost to simple buy-and-hold. A method needs to justify itself against "doing nothing," not against zero.
- A high win rate doesn't mean profit. RSI2 and Stochastic were "right" in most trades and earned the least. The Turtles were right a third of the time and matched the index. What matters is the relative size of average gain versus average loss, not the count.
- One year can tell the whole story. Momentum did +114% In 2024, the chip year, and the rest of the years were ordinary. A universe selected today contains today's winners, which is exactly what momentum likes. Don't rely on this number.
- Maximum drawdown is the price. -32% On paper it looks reasonable; living through it with real money, while everything looks broken, is exactly why most people abandon a strategy right before it recovers.
What to measure, so you don't get fooled
Four numbers tell almost everything, and whoever shows only one of them is hiding the rest:
- Expectancy per trade in R: How much you earn on average in units of the predefined risk. Positive after costs, or there's no business. What is R.
- Profit Factor: Total gains divided by total losses. Below 1.0 you're losing; 1.2 to 1.5 is a real business; above 3 in backtesting it's usually a bug.
- Maximum drawdown: The biggest drop from the peak. That's what you'll feel, not the annual return.
- Number of trades: Below 100 trades, the other numbers are luck. The Golden Cross in the table above was measured on only 28 trades, which is why its row is weaker than the others.
And where does TechAnalyzer Pro fit into the picture
Our engine runs one family from the list: swing trading on price structure breakout, on closed daily candles, long only, with a stop and target set in advance and with an 200 moving-average regime filter. The track record of every signal, including the losses, is published on the site. The other methods here are shown so you know what they are, what they cost, and what they aren't. The table can be rerun, and that's exactly what we did before writing it.
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