Nasdaq-100 Futures
Long-Only Trading Strategy
Every Trade. Every Test. Every Calculation.
This is one completed MPM Research Project.
It documents a long-only strategy on Nasdaq-100 futures — found, tested, challenged, cross-validated on a second engine, and published with every number reproducible from raw data.
The strategy is one outcome of the research. The real value is the methodology, the testing, and the evidence behind every conclusion.
Every conclusion. Every trade. Every calculation. Every verification. Included.
No hidden indicators. No black boxes. No unverifiable claims.
Get the Complete Research Project
Every document, the full trade database, the source code, and the verification tools — yours to keep. Fully credited toward the Intelligence Circle if you upgrade later.
TradingView scripts included — so you can see and track the setup on your charts.
Same strategy. Same 187 trades. Same signals. Two ways to enter — choose whichever matches your execution style.
Place a market-on-close order before the session ends · Cross-validated 181/181 trades · $0 discrepancy
~22% gap = overnight gap cost between the two fills
Place a market-on-open order the following morning · More conservative · No monitoring required
* Monte Carlo: 10,000 resamples testing robustness — a robustness check, not proof of future results. 187 trades · 26 years (Jan 2000 – May 2026) · 1 contract · gross of commissions.
Same-bar-close = entry at signal day's close (market-on-close order). Next-bar-open = entry at next morning's open (market-on-open order). The ~22% gap is overnight gap cost. Both are documented side by side throughout every file in this kit.
This Strategy Shows How We Do Research
Most trading products only show the final answer. MPM shows the complete research process.
Every idea begins with a market observation, becomes a research question, is tested on historical data, validated under different market conditions, challenged with robustness testing, independently verified, fully documented — and only then published.
This strategy is simply one completed MPM Research Project. The same methodology governs every project published inside the Intelligence Circle.
Read This Honestly
The dollar profit here — roughly $1,965/year (next-bar-open) or $2,503/year (same-bar-close) averaged across the full 26 years on one contract — is not the point.
That 26-year average also flattens something worth seeing on its own. As the transparency section notes, the 2019–2023 stretch produced about half the total return — and the years since 2019 have run well above the long-run average, closer to $4,700–$6,000 per year per contract depending on the fill.
There is a simple, structural reason the recent dollars are larger: the Nasdaq-100 itself is far higher than it used to be. The strategy captures a roughly similar percentage move each time — but the same percentage is worth many more dollars as the index rises. Where NQ actually traded at each point in time:
| Around | NQ level | Value of a 1% move (1 contract) |
|---|---|---|
| 2005 | ~1,660 | ~$330 |
| 2015 | ~4,590 | ~$920 |
| 2019 | ~8,750 | ~$1,750 |
| 2024 | ~21,200 | ~$4,250 |
| Early 2026 | ~27,800 | ~$5,560 |
A 1% move that was worth a few hundred dollars two decades ago is worth several thousand at recent levels. So the same edge, applied at where NQ trades now, simply moves more dollars per trade than it used to. We are not promising the recent pace continues — regimes change, and a slower period is entirely possible — but the larger recent dollar figures are mostly a function of where the index trades today, not a different or better strategy.
This package exists to show you how we research, validate, and document a market edge from beginning to end, with nothing hidden: the profitable years and the flat ones, the signal that worked and the instruments where the same idea was rejected, the optimistic fill accounting alongside the conservative one that leads every table.
Every assumption is documented. Every calculation can be inspected. Every published result can be reproduced.
187 Trades. 26 Years. One Contract.
| Metric | Same-Bar-Close | Next-Bar-Open |
|---|---|---|
| Hold period | 1 session (~1 day per trade; capital is free when not in a position) | |
| Frequency | ~7 trades per year, low-frequency | |
| Total trades | 187 | 187 |
| Win rate | 61.5% | 57.8% |
| Total net P&L | +$65,080 | +$51,065 |
| Expectancy per trade | +$348 | +$273 |
| Average win / average loss | +$1,136 / −$910 | +$1,067 / −$812 |
| Largest single win / loss | +$7,375 / −$6,070 | +$6,445 / −$5,980 |
| Profit factor | 1.99 | 1.80 |
| Max consecutive losses | 5 | |
| Max drawdown (single backtest path) | −$6,240 | −$6,015 |
| Max drawdown (bootstrap median) | −$10,790 | −$10,650 |
| Top-5 winners (both) | +$27,245 (53% of net profit next-open / 42% same-bar) | |
| Profit factor without top-5 | 1.57 | 1.37 |
Same-bar-close is the cross-validated accounting — matched 181/181 trades against an independent TradingView engine with $0 discrepancy. Next-bar-open is the more conservative method requiring only an ordinary next-morning fill. Both are valid; the gap between them is overnight gap cost (~22% of net P&L).
- Net P&L
- +$51,065
- Profit factor
- 1.80
- Win rate
- 57.8%
- Entry
- Ordinary market-on-open fill
- Net P&L
- +$65,080
- Profit factor
- 1.99
- Win rate
- 61.5%
- Entry
- Fill at exact signal price
The ~22% gap between the two accountings is overnight gap cost — the price of using an honestly achievable fill. Both are reported side-by-side in every document in this kit. The conservative one leads everywhere.
No indicators. No chart patterns.
Just statistical computation.
This strategy does not use a single technical analysis indicator — no moving averages, no RSI, no MACD, no oscillators, no chart patterns, no discretionary reading of the tape.
Every entry is decided by a statistical rule computed directly from price. That is the entire signal — arithmetic on price data, not a subjective interpretation of a chart.
Python
The strategy is computed and validated in Python — the raw statistical work, the backtest, the Monte Carlo, the randomization tests. This is the source of truth for every published number, and the full source code is included so you can inspect and re-run every calculation.
TradingView Pine Script
The included Pine Script is a visualization tool — it draws the strategy's entries, exits, and equity curve directly on a TradingView chart. It exists so that traders who don't work in Python can still see the strategy play out visually and inspect it bar by bar, without touching a line of code.
The Python engine is the authority; the Pine Script is there so the same logic can be seen on a live chart. Both are included, and on the Nasdaq-100 project they were reconciled trade-by-trade — 181 of 181 common trades matched exactly.
Two Engines. 181 Common Trades.
Zero Discrepancy.
The strategy was re-implemented from scratch in Pine Script — an independent second engine — and run on TradingView's own Nasdaq-100 futures data.
On the 181 trades common to both engines, entry prices and per-trade P&L matched exactly. To the dollar. Total discrepancy: $0.
| Python engine | TradingView engine | |
|---|---|---|
| Window | Jan 2000 – May 2026 | Jan 2000 – Apr 2026 |
| Trades | 187 | 185 |
| Net P&L (same-day-close) | +$65,080 | +$55,805 |
| Profit factor | 1.99 | 1.78 |
| Win rate | 61.5% | 60.5% |
| Matched common trades | 181 of 181 — identical prices, identical P&L, $0 discrepancy | |
Performance through every major market event since 2000:
| Market period | Trades | Result |
|---|---|---|
| Dot-com crash (2000–2002) | 8 | +$3,890 |
| Financial crisis (2008–2009) | 10 | +$2,110 |
| COVID crash year (2020) | 8 | +$6,720 |
| 2022 bear market | 5 | +$8,180 |
| Losing years (full 26-year record) | 2000 · 2006 · 2011 · 2014 · 2016 · 2019 | |
21 of 27 calendar years positive (78%). No losing year exceeded -$2,895. All rows computed from the included trade database — nothing asserted that cannot be recomputed.
The Details Behind the Numbers
Every research result has a fuller story than a single headline can tell. Here is the context behind these numbers, stated plainly — all of it documented in full inside the kit.
Every multi-year block was profitable
Each 4–5-year block since 2000 was positive, and the strongest stretch (2019–2023) contributed about half the total return. Some periods were much quieter than others — that variation across time is normal for a low-frequency strategy, and the full regime breakdown is included so you can see exactly how each period performed.
The edge survives without its best trades
Like most strategies, a handful of strong trades contribute an outsized share of the total. Even with the five best trades removed entirely, the strategy stayed profitable (profit factor 1.37). The complete trade database is included so you can see the full distribution of winners and losers.
Tested across 15 different parameter values
The production setting was not tuned to this data — it is the same standard parameter we apply across our research, chosen before the backtest was run. We then tested 14 other values around it, and all 15 were profitable, with a stable win rate of 54.9%–58.0% throughout. The complete parameter sweep is included so you can see every value and how the results respond.
Sized to the distribution, not one path
The single historical backtest shows a max drawdown of −$6,015. Because that reflects one specific ordering of trades, we also run a 10,000-path resampling: the typical drawdown is around −$10,650, with a challenging-case figure near −$20,485. Those are the numbers we recommend sizing to, and the full Monte Carlo analysis is included.
One of the two signals was discovered by studying a strategy that did not work — a short that failed clearly enough to be a useful signal when inverted. The whitepaper documents that origin in full, including the independent research that supports it and the instrument where the same idea was tested and set aside. We include that story because how a finding was reached is part of judging it.
Every published research project must survive
independent validation before publication.
Randomization benchmark: the result beats 99.1% of 10,000 random-direction simulations and 89.1% of 10,000 random-entry-day simulations — the second, harder test nets out the index's own upward drift, and is the number that leads.
Every research project follows the same documented workflow.
Every published project follows the same documentation standard.
Methodology Whitepaper
Every assumption, filter, exclusion and limitation — documented so another researcher can independently reproduce the study.
How We Found It
The original observation, the failed short that became half the signal, and every idea tested and rejected along the way.
Teaching Guide
Learn to identify, log and evaluate the strategy setup yourself — including what forward results would look like if the edge has weakened.
Rules Sheet
The complete one-page execution checklist — exactly what was tested, with no discretion hiding inside the results.
Historical Trade Database
All 187 trades, both fill accountings, every price, runup and drawdown per trade. Nothing removed. Nothing cherry-picked.
TradingView Pine Script
The independently-coded strategy that matched the Python engine 181-for-181 — inspect the rules bar-by-bar on a live chart.
Python Source Code (4 scripts)
Core backtest, randomization tests, Monte Carlo, and deep-dive analysis — inspect every calculation behind the published research.
Verification Scripts
Independently regenerate every published statistic without trusting MPM's calculations.
Monte Carlo Analysis
10,000 resampled paths — both fill accountings — with the drawdown distribution that shows what the single backtest path cannot.
Regime Appendix
Where the edge lived, where it softened, and every stress test including the rejected ones — crude oil, silver, and the gold extension.
Randomization Tests
Both benchmarks — random direction and random entry-day — with the drift-corrected test given top billing and full explanation.
Verification README
Step-by-step guide to reproduce every published figure from raw data to final statistic — no gaps, no assumed knowledge.
Most trading businesses ask you to trust their conclusions.
We prefer to publish enough evidence for you to challenge them.
If our research is wrong, you should be able to prove it.
That is why every research project includes methodology, historical trades, source code, verification scripts, and robustness testing.
Our goal is not to persuade you. Our goal is to make our conclusions independently reproducible.
Rejections, in practice.
The same core idea was tested on four instruments. Crude oil produced a profitable-looking backtest — but its five best trades exceeded 100% of total profit, so there was no residual edge underneath, and we did not publish it. On silver, the entry filter actively rejected the strongest signals — a real effect, but the wrong filter for that instrument, so it was excluded. On gold, an extension that works on NQ failed the regime test and was cut. This is what the standard looks like in practice: a good-looking result is not a finding until it survives testing.
One Research Project.
Years of Research Inside the Intelligence Circle.
This Research Project
- One completed study
- One market idea
- One documented methodology
- One historical database
- One validation package
Intelligence Circle
- New markets and hypotheses
- Execution technique research
- Trading strategy development
- Reaction Library & Backtest Library — lifetime access
- The Intelligence Vault of research packages
- Tested and rejected hypotheses
Your purchase of this research project is credited in full toward Intelligence Circle membership — you only pay the balance.
Questions, answered.
Is this a signal service?
Do I receive buy/sell alerts?
Does this strategy use technical indicators?
Why is there a TradingView Pine Script if the strategy is in Python?
Can I reproduce every published statistic?
Why do you show two different profit numbers?
What happened when you tested the near-low signal as a short?
What if the research is wrong?
Why include rejected research?
What happens after the dataset ends?
What is the Intelligence Circle?
Ready to Explore the Complete Research Project?
Everything included. Nothing hidden.
One payment. Lifetime access.
$29 fully credited if you later join the Intelligence Circle.
Read the methodology. Review every historical trade. Run the verification scripts. Execute the Monte Carlo analysis. Inspect the source code. Challenge every conclusion.
If the evidence convinces you, you'll understand why the Intelligence Circle exists. If it doesn't, you should keep your money.
That's how independent market research should work.
Want both? Get the S&P 500 + Nasdaq-100 combo
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S&P 500 Futures Long-Only Strategy
A deliberately selective, low-frequency long-only strategy on S&P 500 futures — results net of costs, corroborated across 26 years on a second engine.
Hypothetical performance disclosure. All results shown are simulated historical backtests, not actual trading, and are gross of commissions and slippage unless stated otherwise. Hypothetical performance results have inherent limitations: they do not represent actual trading, may under- or over-compensate for market factors such as liquidity, and are designed with the benefit of hindsight. No representation is made that any account will or is likely to achieve similar results. Futures trading involves substantial risk of loss and is not suitable for all investors. Past performance, real or hypothetical, is not indicative of future results. Nothing on this page is investment advice.