Multifactor
C+ 72 completed
Ai Ml
unknown / python · small
58
Files
5,793
LOC
0
Frameworks
6
Languages
Pipeline State
completedRun ID
#361932Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
34.11Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47727
Member of a group with 1 similar repo(s) — canonical #18123 view group →
Top concepts (1)
Data/ML
Open data scored by Repobility · https://repobility.com
AI Prompt
Create a comprehensive, pure backtesting research platform in Python for BTC-USDT-SWAP perpetual contracts on OKX. The system needs to handle the entire workflow: first, fetching and cleaning OHLCV data (1m/5m) from OKX, ensuring data quality, and storing it in Parquet format. Next, it must calculate and manage factors, including built-in ones like 'momentum' and 'reversal'. Following signal generation, the platform needs modules for risk management, signal combination, and finally, the core backtest engine. The backtest must simulate execution, calculate metrics like Sharpe Ratio and Max Drawdown, and provide factor contribution attribution. Please structure the code to follow the provided modular design.
python backtesting finance quant okx data-science trading-strategy
Generated by gemma4:latest
Catalog Information
This project is a multifactor backtest research platform for BTC-USDT-SWAP perpetual contracts on OKX, designed to evaluate trading strategies without live or simulated trading.
Description
The mockingbird-gan__multifactor project is a Python-based tool for backtesting trading strategies on the OKX exchange. It provides a multifactor approach to evaluating trading performance, using a combination of technical indicators and risk management techniques. The platform allows users to define their own trading strategies and evaluate their performance on historical data.
الوصف
هذا المشروع هو منصة بحث واختبار متعدد العوامل ل العقود المستمرة BTC-USDT-SWAP على بروكسي، مصممًا لتقييم استراتيجيات التداول دون تداول حقيقي أو محاكاة
Novelty
7/10Tags
backtesting trading-strategies technical-indicators risk-management multifactor-analysis okx-exchange perpetual-contracts
Technologies
matplotlib numpy pandas scikit-learn scipy
Claude Models
claude-sonnet-4.6 claude-opus-4.6
Quality Score
C+
71.5/100
Structure
64
Code Quality
79
Documentation
64
Testing
50
Practices
79
Security
100
Dependencies
60
Strengths
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- 321 duplicate lines detected \u2014 consider DRY refactoring
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
Security & Health
7.8h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (3)
Repobility analyzer · published findings · https://repobility.com
Unknown
License
1.6%
Duplication
Languages
Frameworks
None detected
Concepts (1)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Data/ML | data-ml | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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