Great algorithmic systems are methodical, boring, and time-consuming to build.
That is what we build at Tesseract Research: quantitative trading systems shaped through alpha research, mathematical techniques, validation, execution logic, and risk design.
Not trading bots. Quantitative system design.
The value is not coding a trading idea into a button. The value is using quantitative and mathematical techniques to find, test, reject, and engineer an edge that can survive market conditions that were not designed to be friendly.
Strategy thesis design
We start from market behavior, not indicator stacking. The work studies where a repeatable edge may actually form.
- Market structure hypotheses.
- Supply and demand imbalance logic.
- Fractal behavior across timeframes.
Alpha research
Alpha factors are treated as hypotheses. They must survive context, regime behavior, false positives, interaction effects, and decay.
- Entry conditions and rejection logic.
- Factor interaction and conflict checks.
- Robustness against shallow pattern mining.
Backtesting and validation
A backtest should expose weakness, not flatter the strategy designer. The system is tested for fragility before it is treated as serious.
- Regime sensitivity.
- Drawdown behavior.
- Assumption and overfitting checks.
Execution and risk architecture
The system is engineered around survival first. Win rate is not the idol. Risk structure is.
- Position and exposure rules.
- Execution windows and exit logic.
- Automation-ready system design.
From market mechanism to system rules.
A serious trading system is not a pile of indicators. It is a structured attempt to express market behavior through quantitative and mathematical techniques, test those rules against uncertainty, and reject what only worked because the backtest was forgiving.
1. Identify the market mechanism
Isolate the behavior the system is supposed to exploit, such as imbalance, acceleration, regime pressure, or volatility compression.
2. Translate behavior into testable rules
Turn the thesis into entry logic, rejection logic, sizing constraints, exit structure, and conditions where the system should stay silent.
3. Validate across regimes
Study whether the idea depends on one friendly market phase or can survive changing volatility, trend, liquidity, and correlation environments.
4. Engineer execution and risk
Define exposure, drawdown limits, trade frequency, execution assumptions, and the practical constraints that separate a model from a usable system.
5. Decide whether it deserves capital
Some strategies should be built. Some should be rejected. The process is designed to make that distinction before real risk is taken.
Private evidence. Public discipline.
Serious quantitative work needs assumptions, limitations, and direct review. A system cannot be judged responsibly from a few numbers alone, and the wrong client should be filtered out before a project begins.
| Tested CAGR | 45.24% |
|---|---|
| Maximum drawdown | 5.07% |
| Average annual drawdown | 3.73% |
| Sharpe ratio | 2.28 |
| MAR ratio | 8.92 |
What these numbers are, and what they are not
These figures describe one proprietary futures strategy framework in a tested research environment. They are included to show the standard of work, not to imply that future systems will replicate the same result.
Full context, assumptions, limitations, and evidence can be reviewed directly with qualified prospective clients.
Good fit
- High net worth investors or capital groups seeking serious systematic research.
- Clients who understand drawdown, uncertainty, and execution constraints.
- Multi-asset projects where risk design matters as much as return.
- Investors willing to review evidence, assumptions, and limitations directly.
Not a fit
- Anyone seeking guaranteed profits or a black-box money machine.
- Martingale, gambling, or unrealistic ultra-short-term systems.
- Clients who only want a bot coded around an untested idea.
- Projects where the market, timeframe, or infrastructure makes the service unsuitable.
Responsible investor disclaimer
This service is offered to responsible investors and qualified prospective clients. Algorithmic trading systems are designed and built with the understanding that they are not perfect, cannot remove uncertainty, and do not guarantee profit. Tesseract Research commits to giving each accepted client its absolute best work, but this service is not financial advice, not investment advisory, not a trade-alert service, and not a guarantee of future performance.
Direct review is required before accepting a project because not every market, timeframe, capital base, risk tolerance, or infrastructure setup is suitable for this service.
Request a private review