BUILD / 01 — PRODUCT OVERVIEW
MULTI-STRATEGY QUANTITATIVE TRADING ENGINE
APEX QUANT ENGINE
Multi-strategy quantitative trading engine.
Apex is a multi-strategy quantitative trading engine spanning BTC, ETH and SOL. It couples a market-data engine, a strategy engine, a risk engine, portfolio allocation and deterministic backtesting with paper execution — 11.9M+ one-minute bars and 5,867 completed backtest trades.
+570.18%
NET SIMULATED ROI
BACKTEST
MASTER-FUND BACKTEST
Self-reported master-fund backtest after modeled fees and slippage; evaluation window and seed not documented
5,867
COMPLETED BACKTEST TRADES
BACKTEST
FULL-HISTORY SIMULATION
11.9M+
1-MINUTE BARS PROCESSED
BACKTEST
BTC / ETH / SOL
44.50s
BACKTEST EXECUTION TIME
SELF-BENCHMARK
MASTER-FUND RUN
Self-reported single-run benchmark; machine specification not documented
SYSTEM INSTRUMENT / APEX
VISUAL METAPHOR — PORTFOLIO / STRATEGY / EQUITY LANDSCAPE
INSTRUMENT / EQUITY SURFACE
ILLUSTRATIVE — SEEDED DETERMINISTIC SHAPE, NOT THE BACKTEST ARTIFACT
The surface demonstrates the shape of a multi-strategy equity trajectory with its drawdown topology. Hover to inspect any point. The measured master-fund result appears in the results chapter below — a backtest outcome at a single point in history, not a forecast.
THE BUILD AT A GLANCE
WHAT IT IS, WHAT IT SOLVES, HOW IT IS BUILT — THE FULL EVIDENCE CHAIN LIVES IN THE CASE STUDY
THE PROBLEM
Retail-grade backtesting tools make it easy to produce numbers that cannot be reproduced. Lookahead bias, implicit assumptions about fills and fees, and non-deterministic data access quietly inflate results.
Worse, most frameworks couple strategy logic to execution logic, so the code that generated a backtest is not the code that would trade — the simulation and the system drift apart.
WHAT IT DOES
- Build a single engine where research, simulation and (paper) execution share one deterministic code path.
- Enforce risk at the portfolio level before any order is constructed.
- Make every backtest reproducible from raw data to final equity curve.
RESEARCH QUESTION
Can a multi-strategy crypto portfolio, simulated over millions of one-minute bars with explicit fees, slippage and hard risk caps, remain net-positive after all modeled costs?
ARCHITECTURE
ARCHITECTURE — DATA PATH
ARCHITECTURE DIAGRAM — THE DOCUMENTED LAYERS, NOT A MEASUREMENT
- 01
DATA ENGINE
Market-data ingestion and normalization
- 02
STRATEGY ENGINE
Multi-strategy signal generation
- 03
RISK ENGINE
Pre-trade risk enforcement
- 04
PORTFOLIO ALLOCATION
Cross-asset capital distribution
- 05
EXECUTION
Deterministic fills and paper execution
- 06
BACKTEST CORE
Deterministic simulation harness
TECHNOLOGY
KEY EVIDENCE
BACKTEST ARTIFACTS
Equity curves, trade logs and run configurations reproducible from the repository
OPEN THE SOURCE →PAPER MODE
Forward paper-execution mode runs the identical code path
CURRENT STATE
BACKTEST · PAPER
Deterministic backtesting and paper execution. Not live trading.
GO DEEPER
THE FULL EVIDENCE CHAIN
Problem, hypothesis, architecture, methodology, experiments, results — and the failures and limitations beside them. The case study is the full write-up for this build.