Support and resistance zones, generated by a mathematical model — every reaction studied across 4 years of silver futures.
1,233 documented examples. Every level. Every outcome. Every lesson.
Built for intraday, swing, and position traders studying silver futures.
56.5% of winning trades never went into drawdown — see how, in every single setup.
From $49 · one-time · lifetime options available
You think you know your strategy.
But without data, you're only remembering the trades that worked.
Most traders carry a mental highlight reel — the wins they remember, the painful losses, a few "I called it" moments. What's missing is the full record.
The setups that almost worked. The ones that quietly stopped out. The patterns that look obvious in hindsight but blended into noise in real time.
This library is that full record. 1,233 trades. Every entry. Every exit. Every loss. Studyable, filterable, teachable.
Not signals. Not predictions. A research and learning archive.
1,233 dark-themed charts. Entry, stop, target, runup, drawdown. Every level visible.
Each trade pre-scored on a 7-9 quality scale. Filter by score. See which setups have the cleanest entries.
By zone (S1/S2/S3/R1/R2). By outcome (bounce/break). By year. By score. By first-touch behavior.
Equity curve. Monthly heatmap. Distribution charts. By-zone breakdowns. By-score comparisons.
Each trade includes concise, structured observations — not generic AI commentary. Pre-generated for every setup.
Found a pattern? Save it. Annotate it. Reference it later. Build your own study library.
One payment. Full archive. All updates as we add data.
The library isn't theory. It's pattern recognition fuel.
Study 1,233 documented setups across all zone types. Train your eye to spot the structural conditions that precede clean reactions.
See which Statistical Score levels lead to clean wins versus which ones bleed before paying. Skip the ones that don't fit your criteria.
838 losing trades documented in full detail. Learn the conditions under which support and resistance break — so you stop expecting bounces that won't come.
Watch how a 1:3 risk-reward ratio plays out across hundreds of real setups. See exactly how asymmetric payoffs compound over time, even at 32% win rate.
The library doesn't tell you "enter here, exit here." It does something better — it shows you 1,233 documented examples of these zones in action, and lets you build your own pattern recognition.
By the time you've studied a few hundred setups, you'll see things you didn't see before:
You'll spot them in live charts before they happen — not after.
838 losing trades are documented in full. You'll learn the warning signs.
The data shows you which structural conditions support continuation versus reversal.
A Score 9 trade earns 12x the expectancy of a Score 7 trade — you'll learn to wait for the difference.
No rigid rules. No black-box system. No "follow these signals." Just pattern recognition built from 1,233 real examples — the same way professional traders learn.
The Backtest Library isn't a one-size-fits-all signal feed. It teaches you what you specifically need to learn — based on how you trade.
Win rate is a vanity metric without context. 32% wins at 1:3 RR makes more money than 70% wins at 1:1 RR. The library teaches you why — and how to apply it to your style.
Same risk, same number of trades — Strategy B's asymmetric payoff wins, despite a much lower hit rate.
You'll come away knowing: which intraday zones are worth waiting for, and which ones are statistical noise.
You'll come away knowing: which zones are worth holding overnight, and how to maximize R-multiples on the ones that work.
You'll come away knowing: which structural levels deserve position-sized commitment, and which are just noise dressed as structure.
You'll come away knowing: how professional traders actually evaluate setups — not the YouTube version.
The library is a research and educational tool. It does not make trading recommendations specific to your situation, and we are not licensed financial advisors. How you apply what you learn is your decision.
Anyone can show profitable backtest numbers. The real question is: is this edge real, or is it luck?
We tested the strategy against four formal statistical benchmarks. Here's what the data says.
A standard t-test on per-trade expectancy: p-value = 0.0003
There is a 99.97% probability that the strategy's profitability reflects a real edge — not random luck. Passes the strictest scientific significance threshold (p < 0.001).
At a 1:3 risk-reward ratio, random entry needs ~25% win rate to break even. The strategy hits 32.04%.
Edge over random: +7 percentage points. Translated to dollars: 4.3x the profitability of random entries across the same 1,233-trade sample.
| Year | Trades | WR | Net PnL |
|---|---|---|---|
| 2022 | 218 | 32.1% | +$23,240 |
| 2023 | 321 | 32.1% | +$30,610 |
| 2024 | 301 | 29.6% | +$29,661 |
| 2025 | 303 | 34.0% | +$95,810 |
| 2026 (partial) | 90 | 33.3% | +$175,636 |
Every calendar year profitable. Edge doesn't depend on a single lucky year.
If the scoring system is meaningful, higher scores should produce better outcomes. The data confirms it:
| Score | Trades | Win Rate | Avg PnL/Trade |
|---|---|---|---|
| 7/9 | 1,070 | 31.3% | $179 |
| 8/9 | 107 | 35.5% | $370 |
| 9/9 | 56 | 39.3% | $2,206 |
A Score 9 trade earns 12x the expectancy of a Score 7 trade.
This isn't a vanity metric. It's a measurable, replicable signal that tells you which setups carry the highest probability of clean, profitable execution — exactly the pattern recognition the library teaches you to develop.
This is what separates a research and education product from a signal service. We didn't write a sales page first and back-fit the numbers. We ran the math, found the edge, and documented it for you to verify — and to learn from.
Why is the win rate 32%?
Because we target 3x our stop distance. At wider targets, fewer trades reach them — but each win pays 3x what each loss costs. This is asymmetric payoff trading, and the math works.
When MPM-SCM wins, it wins clean.
The math doesn't need a 70% win rate to compound capital. It needs asymmetric payoffs — risking $1 to make $3, repeated across a large enough sample for the edge to play out.
That's what this library teaches. Every loss disclosed. Every assumption transparent.
A 32% win rate forces you to confront the math directly. You cannot rely on "feeling right often" — you have to trust the expectancy. That discipline is what separates retail traders from professional ones.
| Metric | Value | What it means |
|---|---|---|
| Statistical Significance | p < 0.001 | 99.97% probability edge is real, not luck |
| Recovery Factor | 9.29x | Net profit was 9x larger than worst drawdown |
| Profit Factor | 1.60 | Gross profit divided by gross loss |
| Realized Win/Loss Ratio | 3.4x | Average win compared to average loss |
| Expectancy per trade | $288 | What an average trade contributed |
| 95% Confidence Interval | [$134, $442] | Range expectancy likely falls in |
| Max DD as % of gain | 10.8% | Worst drawdown was 11% of total net profit |
| Profitable months | 84% (41/49) | Most months were net positive |
| Worst month | -$2,421 | November 2023 |
| Best month | +$90,373 | February 2026 |
| Avg holding time | 3.2 hours | Trades resolve in same session typically |
| Beats random entry | +7pp WR | At same 1:3 risk-reward |
All metrics derived from backtest data assuming next-bar-open execution with no slippage. Live trading performance will differ.
Click any chart to enlarge. These are the same charts you'll study in the full library.
Price tagged S1 zone bottom, never dipped below entry, ran $3,450 favorably.
S2 support failed on second test. The library documents every loss this clearly.
R2 resistance held. Short entry filled at zone top, target reached without retest.
If any of those apply, this isn't the right product. We'd rather you not buy than be disappointed.
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1,233 documented setups. Every entry. Every exit. Every loss. Every win.
A 4-year window into how levels actually behave — and how to read them.
Built for traders who want to learn how the zones work, not gamble on what they hope will happen.
Launch price · One-time payment · Lifetime access · Pricing increases as data expands