Who We Are

Automated NinjaTrader Strategies Built by Experienced Traders

Falco was built by experienced traders with a deep focus on algorithmic strategy development. We've spent years studying market microstructure, analyzing performance across bull and bear cycles, and building systematic frameworks for disciplined execution. Drawing on that research, we develop automated trading strategies that cut through the noise, systematically manage risk, and focus on real execution, not just theory.

Our mission is to apply the same quantitative rigor and systematic principles used by top institutions to give independent traders robust, disciplined tools for consistent market performance.

100+ Strategies Tested 90–95% Rejected 11-Stage Validation Pipeline Walk-Forward Tested

The FalcoAlgo Research Gauntlet

We test more strategies than we publish — by a wide margin. Every idea runs an 11-stage automated pipeline before it gets anywhere near a live account. A backtest is not a trading edge. It's a historical simulation — and how it's constructed determines whether it tells you anything useful at all.

The Typical Approach
  • Single backtest on hand-selected dates — the best-looking window
  • No out-of-sample validation — the strategy has never seen "new" data
  • Commission and slippage stripped from the performance numbers
  • No risk controls — just raw P&L curves and peak drawdown
  • Parameters tuned to look good on the exact data used to test them
The FalcoAlgo Approach
  • 3+ years of in-sample data, then tested on separate out-of-sample periods
  • 5-fold walk-forward validation — the edge must survive on data it's never seen
  • Real commission and slippage included in every backtest result
  • 7 automated risk gates — profit factor, drawdown, risk of ruin, concentration
  • Parameter robustness tested — we look for exits that work, not ones that look best

11 stages. Fail any one of them — the strategy is killed.

Each stage is a filter. The pipeline runs in sequence. There are no shortcuts, no exceptions, and no overrides without a documented rationale. If a strategy makes it to Stage 11, it has been interrogated from every angle we know how to test.

01
Research
Specialists derive signals from first principles and academic literature. Every signal gets a red-team attack — if the hypothesis can't survive internal scrutiny, it doesn't proceed.
Research
02
Hypothesis Validation
Every idea is peer-reviewed before any code is written. Entry logic, exit structure, expected trade frequency, and pre-registered kill criteria are locked in at this stage.
Review
03
Zombie Check
Cross-referenced against a full archive of prior tests. If this exact signal combination has been tested before and failed, it's killed automatically — no resources wasted retesting dead ideas.
Automated
04
Code Audit
Automated code review runs before any backtest. Logic errors, implementation bugs, and calculation mistakes are caught here — not after interpreting flawed results.
Automated
05
Full Backtest
3+ years of historical data with real commission and slippage applied. Trade count must meet a minimum frequency threshold. Strategies with too few trades are killed — not enough data to draw conclusions from.
Automated
06
MAE / MFE Gate
Per-trade excursion analysis. Maximum adverse excursion (how far against you trades go) and maximum favorable excursion (how far in your favor before reversal) are measured across every trade to assess structural edge quality.
Automated
07
Exit Optimization
Exit parameters are swept across a defined range — never skipped. We look for robust exits across a parameter space, not the single best-looking combination. Strategy type determines exit structure: trend-following exits are built differently than mean-reversion exits.
Automated
08
Behavioral Audit
A 20-check forensic sweep across five categories: directional bias, temporal quirks, price action anomalies, indicator behavior, and statistical impossibilities. Any high-severity flag kills the strategy.
Automated
09
Walk-Forward Validation
5-fold anchored walk-forward testing. The strategy is repeatedly trained on one period, then tested on data it has never seen. Out-of-sample performance must hold up across multiple separate windows — not just one cherry-picked period.
Automated
10
Risk Kill Gates
7 sequential automated gates: profit factor (commission-adjusted), drawdown-to-annual-profit ratio, statistical significance test (DSR), risk of ruin, single-trade concentration, Monte Carlo confidence interval, and bootstrap validation. Any single failure kills the strategy.
Automated
11
Final Review
A pre-mortem checklist across 12 items: commission model accuracy, drawdown thresholds appropriate for the strategy type, full statistical validation complete, no single-trade dependency, out-of-sample performance confirmed, and correlation check against any currently deployed strategies.
Review

Most strategies die before they get anywhere near a live account.

That's not a failure of the process. That's the process working exactly as it should.

90–95%
rejection rate

Out of every 100 ideas tested, 90 to 95 are killed before reaching production.

Strategies are killed for failing any single gate — not because they're close. A strategy that passes 10 gates and fails gate 11 is killed, just like one that fails gate 1. That's how the pipeline maintains consistent standards regardless of how promising an idea seemed at inception.

We track the reasons behind every kill. Those patterns feed back into research — improving what gets developed in the first place.

Killed at the hypothesis stage
Red-team review catches weak signals before any code is written, saving development time on dead ends.
Killed at the zombie check
Ideas that already failed in a prior batch are recognized and killed immediately — the archive prevents repeated dead ends.
Killed at risk kill gates
Strategies that backtest well but fail statistical significance, concentration, or drawdown thresholds don't proceed — good-looking backtests are not sufficient.
Killed at walk-forward
Strategies that perform well in-sample but collapse on unseen data are curve-fit, not edge-based. Walk-forward separates the two.

Every strategy on FalcoAlgo has passed the full pipeline.

If a strategy is available for purchase, it has cleared all 11 stages. No exceptions. We would rather publish fewer strategies than lower the bar.

Backtests include real commission and slippage
Performance numbers reflect actual trading costs. We don't strip out friction to make the equity curve look better.
Walk-forward validated on unseen data
Every published strategy has proven its edge persists across out-of-sample periods — not just on the data it was developed on.
Exits tuned per strategy type
A mean-reversion strategy needs different exits than a trend-follower. Exit parameters are set based on signal type — not applied as a single universal default.
Statistical validation, not just visual backtests
Strategies pass statistical significance testing, Monte Carlo simulation, and bootstrap validation — not just visual inspection of an equity curve.
Behavioral anomalies detected before release
The 20-check behavioral audit catches directional biases, time-of-day quirks, and data artifacts that can make a backtest look better than it is.
Fewer strategies, maintained standards
We don't publish strategies to fill a catalog. Every strategy that makes it through is one that cleared every gate — including the ones most developers skip.
Required Disclosures — CFTC 4.41

Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.

Passing our research pipeline is not a guarantee of future performance. The fact that a strategy has cleared all 11 stages, including walk-forward validation, statistical significance testing, and behavioral auditing, does not mean it will perform similarly in live trading going forward. Past results — whether hypothetical or actual — are not necessarily indicative of future results.

Futures trading involves substantial risk of loss and is not suitable for all investors. The risk of loss in futures trading can be substantial. You should therefore carefully consider whether such trading is suitable for you in light of your financial condition. Read our full Risk Disclosure →

The Problem We Solve

Why We Built FalcoAlgo

Four root causes that prevent traders from performing consistently — and how we address each one.

Problem — Emotions
Fear, greed, hesitation, revenge trading

Even skilled traders break their own rules under pressure. Emotions consistently undercut your edge at the worst possible moments.

Solution — FalcoAlgo

FalcoAlgo executes predefined logic with zero deviation. No second-guessing. No impulse entries. Just rules, enforced.

Problem — Inconsistency
No two trades follow the same rules

Manual traders trade differently every day. Position sizing changes. Stops move. Discipline fades over time.

Solution — FalcoAlgo

Every trade follows the same framework — fixed risk parameters, structured entries, and guardrails that do not bend.

Problem — Slow Reaction
Markets move in milliseconds

Humans react late, chase entries, or freeze during volatility. You miss the entry or take it at the wrong price.

Solution — FalcoAlgo

FalcoAlgo reads conditions and executes instantly. Orders are placed based on logic, not hesitation.

Problem — Lack of Structure
No quantified risk controls

Most traders operate without structured risk controls. One bad trade can erase weeks of progress.

Solution — FalcoAlgo

Built-in risk guardrails: defined stops, position controls, daily limits, and structured execution designed for longevity.

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