Proprietary Quantitative Research Infrastructure

AECQS
AEC Quantitative System

A unified research architecture for discovering, testing, combining and governing quantitative strategy families across market structure, statistical behavior, liquidity, order flow, volatility, momentum, mean reversion, portfolio construction and defined execution controls.
Configuration · Final 10-entryResearch markets · NQ · ES · YMStatus · Active R&DClassification · Simulation
Research classification: AECQS figures shown on this site are historical backtest and simulated research outputs unless explicitly identified otherwise. They are not audited live fund performance, investor returns, or a guarantee of future results. Nothing on this page is an offer to sell or solicitation to buy a security or investment product.
Final Research Configuration

AECQS · 10-Entry Configuration

Net Backtest P&L
+$31,648,982.32

Current cumulative historical simulation.

Accepted Trades
53,317

Historical research trades accepted by the current checkpoint.

Research Markets
3

NQ · ES · YM research universe.

Max Entries / Market
10

Up to 10 simultaneous entries per market.

Cross-Portfolio Max Drawdown
Not calculated

Market-level drawdowns are reported separately; no combined value is inferred.

Research Markets
NQ · ES · YM

Nasdaq-100, S&P 500 and Dow Jones E-mini futures research.

Research State
Active

Architecture and portfolio logic remain under development.

Result Type
Simulated

Not a continuous audited investor track record.

System Architecture

Seven engines. One governed research pipeline.

AECQS separates discovery, implementation, market-state intelligence, signal construction, portfolio controls, execution assumptions and validation so each layer can be tested and challenged independently.
01

Research Engine

Hypothesis generation, feature construction, historical testing and experiment tracking.

02

Strategy Engine

Strategy families, rules, filters, entries, exits, parameters and variant management.

03

Regime Intelligence

Market-state context across trend, volatility, structure, time and participation conditions.

04

Signal & Confluence

Combines independent evidence into rule-based candidate signals and confidence states.

05

Portfolio & Risk

Exposure, sizing, interaction, concentration, drawdown and portfolio-level constraints.

06

Execution Engine

Deterministic order logic with explicit costs, slippage, sizing and implementation assumptions.

07

Validation & Monitoring

Robustness, out-of-sample behavior, stress testing, diagnostics and research-state governance.

Research Domains

Multiple sources of market behavior.

Structure & Liquidity

Price structure, displacement, range behavior, failed continuation, liquidity interaction and location.

Mean Reversion & Distribution

Deviation, normalization, distributional behavior and conditional return-to-location hypotheses.

Order Flow & Volume

Participation, volume concentration and liquidity-response concepts where suitable data is available.

Momentum & Continuation

Persistence, breakout quality, continuation and time-conditioned directional behavior.

VWAP & Market Location

Location relative to volume-weighted reference points, bands and contextual structure.

Strategy Interaction

Portfolio overlap, competing signals, shared risk, correlation and combined-system behavior.

Research Governance

Separate discovery from acceptance.

01
HypothesisDefine the behavior being tested and the conditions under which it is expected to exist.
02
ImplementationEncode deterministic rules, data requirements, assumptions and failure conditions.
03
Historical TestingMeasure behavior across historical samples without treating gross P&L as sufficient evidence.
04
RobustnessChallenge parameters, subperiods, costs, slippage, market states and neighboring implementations.
05
Portfolio IntegrationEvaluate strategy interaction, concentration and portfolio-level risk before acceptance.
06
MonitoringRetain diagnostic visibility after acceptance; research can be revised, restricted or retired.