Quant Research
A 298-Page Thesis Killed Five of Our Ideas and Saved Two
We dragged a PhD on constrained stochastic processes through a crypto scalper's keep/kill program. Most of it died. What survived changed how we trade.
The Library
How We Think About Markets
This is the methodology layer: hypotheses, diagnostics, failure modes, and the papers behind each strategy.
Research Papers & Deep Dives
Quant Research
Fading Liquidation Cascades: A Crypto Scalper That Survived Walk-Forward
Liquidation cascades create predictable overshoots. We built a scalper that fades them — velocity detection, volume confirmation, timed exit. SOL and ETH survived. BTC did not. Charts, data, honest caveats.
Quant Research
Starting from the End: Prop Firm Challenges as Variance Optimization
Most traders approach prop firm challenges looking for alpha. We approached it as a first-passage time problem. €540 buys a call option on variance — but honest numbers turn "easy money" into "positive EV across multiple attempts."
Quant Research
A Timezone Bug Almost Made Me Abandon a Profitable Strategy
Five lines of wrong timestamp parsing turned +350 pips into -870 pips. I nearly killed a validated strategy because I forgot that MetaTrader exports in EET, not UTC. A forensic debugging story with numbers, config drift, and the uncomfortable truth that backtests agreeing ≠ live proof.
Quant Research
From PyTorch to 27 Megabytes: A Neural Net Trade Filter on a Budget VPS
We trained a neural network to filter live trades, deployed it on a $9 VPS, discovered our data provider was hallucinating weekend bars — then removed the NN entirely because 302 trades was never enough to generalize. The deployment engineering was real. The edge was not.
Quant Research
FVG Magnetism: Do Fair Value Gaps Really Fill?
Gap-fill physics is real. 78% of FVGs fill. The math is seductive. We ran it on 42 million ticks and watched the profit evaporate. OHLC daily inflated the profit factor by 4×. The gap fills. You still lose money.
Quant Research
Entropy Collapse as a Volatility Timing Signal
Shannon entropy of price returns drops before big moves. We built a strategy around it. It worked on one pair, one timeframe, and nothing else. This is its obituary.
Quant Research
The Hurst Exponent Lied to Us
We built a regime-detection system around the Hurst exponent. It promised to separate trending from mean-reverting markets. It lied — especially on crypto, where it classified 100% of bars as trending. A postmortem on the most seductive false promise in quant trading.
Quant Research
38 Strategies Tested. 2 Survived.
The honest, updated results from testing 38 trading strategies across entropy, price action, regime detection, chaos theory, crypto microstructure, and signal processing. 36 are dead. The graveyard IS the content.
Strategy Table (Secondary View)
A compact table for quick scanning. The full scoreboard and graveyard live on the Results page.
Strategy PF Pips Trades Verdict
ECVT EURUSD 1H Singha 2025 1.44 +198 44 Inconclusive
Jump Trend EURUSD 1H 1.10 +293 601 ALIVE
FVG 1H Tick 1.04 +466 1504 DEAD
FVG 15m Tick 0.94 -1,453 3217 DEAD
FVG 5m Tick 0.80 -8,509 6842 DEAD
Jump Fade EURUSD 1H 0.53 -2,947 601 DEAD
Hurst Crypto Regime Filter N/A 0 100% DEAD
ECVT Transfer to Equities Singha 2025 N/A -88 9 DEAD
Methodology Findings
OHLC Inflation Is Real
Close-based SL/TP logic can inflate PF by multiples. Tick validation is mandatory.
Distribution Before Direction
Regime and variance shifts often matter more than directional prediction.
Decompose Composite Signals
A composite can hide one useful component under several bad ones.
Transferability Is Rare
Edges built in one asset class usually break in another without redesign.
ECVT Case Study
Entropy Collapse Volatility Timing based on Singha (2025). with strict walk-forward validation.
1.44Profit Factor
41%Win Rate
44Trades
10/20Profitable OOS
2.08xWin/Loss Ratio
Reproduce the Research
Every chart, every backtest, every script is public. Clone it, audit it, and break it if you can.
Open GitHub Repository