All-Weather Crypto Research

Trading as a scientific process,\n powered by multi-scale signals and disciplined risk control.

Since 2017, PG Research has focused on digital assets, combining structured market data, sentiment signals, and macro regimes to build cross-market, cross-horizon hedged portfolios.

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2017 Founded

Multi-Horizon Signal fusion engine

Low-Correlation Strategy clusters

About PG Research

We operate across digital assets, derivatives, and structured portfolios, with research and engineering as core advantages.

Market Microstructure

We monitor order flow, depth, funding, and on-chain behavior to detect structural dislocations and liquidity pulses.

Hedging Framework

We manage drawdowns with basis trades, cross-asset hedges, and volatility strategies to build layered defenses.

Research-Driven Iteration

Experimentation, post-trade review, and deployment form a closed loop to sustain long-term edge.

Strategy System

From multi-scale signal fusion to portfolio construction, we target stable risk-adjusted returns.

Signal Generation

Multi-Scale CWT + LSTM/ARIMA

Wavelet decomposition isolates noise, while statistical models and deep networks capture trends and sentiment shifts.

Signal Fusion

Dynamic Weights + RSI Thresholds

Signals are blended across horizons, with momentum filters reducing false triggers.

Portfolio Management

Cointegration Filters & Layered Hedges

Cross-asset portfolios are monitored for cointegration drift and dynamically re-hedged.

Research Highlights

We focus on reproducible research frameworks and interpretable model outputs.

Multi-Scale Signals

Wavelet + LSTM + ARIMA Fusion Framework

Reduce order-flow noise, separate trend and volatility, and improve signal stability.

Read the paper

Structured Learning

Machine Learning for Structured Data

Feature engineering, model selection, and interpretability for structured datasets.

View repository

Framework Doc

Team, Strategy, and Research Framework

Background, process, and portfolio construction approach.

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Research Methodology

Investment is a repeatable process: observe, hypothesize, validate, review, and execute.

Observation & Hypothesis

Build testable hypotheses around order flow, on-chain behavior, and macro variables.

Validation & Evaluation

Backtests and scenario stress tests assess robustness and limits.

Execution & Review

Real-time monitoring with risk thresholds and structured post-trade reviews.

Technology Support

We provide engineering support for partner platforms and copy-trading infrastructure.

MeiMonkey.capital

Technical support for strategy infrastructure and system reliability.

https://meimonkey.capital

Little Penguin Copy Trading

Technical support for copy-trading operations and performance reporting.

https://www.binance.com/en/copy-trading/lead-details/4682294088813848065?timeRange=180D

Institutional Access

SMA and API integrations for customized mandates and execution pipelines.

Contact the team

Strategic Partners

Infrastructure and liquidity partners across exchanges, brokers, and trading venues.

Performance Snapshot

Latest net value metrics from the copy-trading strategy.

Contact

For strategy research, data engineering, or partnerships, reach us here.

This website is for informational purposes only and does not constitute investment advice or an offer. Digital asset trading involves substantial risk; past performance is not indicative of future results.