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TPTBusiness
229 lines
9.5 KiB
Markdown
229 lines
9.5 KiB
Markdown
# NexQuant
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<p align="center">
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<img src="https://img.shields.io/badge/Python-3.10%20|%203.11-blue?style=for-the-badge&logo=python" alt="Python">
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<img src="https://img.shields.io/badge/Platform-Linux-lightgrey?style=for-the-badge&logo=linux" alt="Platform">
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<img src="https://img.shields.io/badge/Numba-0.59+-00A3E0?style=for-the-badge&logo=numba" alt="Numba">
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<img src="https://img.shields.io/badge/Optuna-4.8+-009B77?style=for-the-badge&logo=optuna" alt="Optuna">
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</p>
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<p align="center">
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<img src="https://img.shields.io/badge/TA--Lib-0.6+-green?style=for-the-badge" alt="TA-Lib">
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<img src="https://img.shields.io/badge/LightGBM-4.6+-00A1E0?style=for-the-badge" alt="LightGBM">
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<img src="https://img.shields.io/badge/Pandas-2.0+-150458?style=for-the-badge&logo=pandas" alt="Pandas">
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<img src="https://img.shields.io/badge/cTrader-OpenAPI-FF6B6B?style=for-the-badge" alt="cTrader">
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</p>
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<h4 align="center">
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<strong>High-Speed Strategy Discovery Framework</strong>
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</h4>
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<p align="center">
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<a href="#quick-start">Quick Start</a> •
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<a href="#strategy-discovery">Strategy Discovery</a> •
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<a href="#live-trading">Live Trading</a> •
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<a href="#features">Features</a>
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</p>
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<p align="center">
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<a href="https://github.com/TPTBusiness/NexQuant/actions/workflows/ci.yml">
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<img src="https://img.shields.io/github/actions/workflow/status/TPTBusiness/NexQuant/ci.yml?branch=master&label=CI&logo=github&style=flat-square" alt="CI Status">
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</a>
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<a href="https://github.com/TPTBusiness/NexQuant/actions/workflows/codacy.yml">
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<img src="https://img.shields.io/github/actions/workflow/status/TPTBusiness/NexQuant/codacy.yml?branch=master&label=Security&logo=shield&style=flat-square" alt="Security Scan">
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</a>
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<a href="https://github.com/TPTBusiness/NexQuant/blob/master/LICENSE">
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<img src="https://img.shields.io/github/license/TPTBusiness/NexQuant?style=flat-square" alt="License">
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</a>
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<a href="https://github.com/astral-sh/ruff">
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<img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json&style=flat-square" alt="Ruff">
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</a>
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<a href="https://github.com/TPTBusiness/NexQuant/commits/master">
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<img src="https://img.shields.io/github/last-commit/TPTBusiness/NexQuant?style=flat-square" alt="Last Commit">
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</a>
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</p>
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---
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## Overview
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**NexQuant** discovers profitable trading strategies through high-speed search — no LLM required. Core engine: Numba JIT-compiled backtest at **735 million bars/second** (245× faster than pandas). Four discovery methods run in a continuous loop:
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| Method | Frequency | Description |
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|--------|-----------|-------------|
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| **Explore** | 30% of iterations | Random strategies from 17 TA-Lib indicators across timeframes |
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| **Exploit** | 70% of iterations | Mutate the best-known strategy (change params, indicator, or timeframe) |
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| **Optuna** | Every 500 iterations | 20-trial hyperparameter optimization on the current best |
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| **LightGBM** | Every 2000 iterations | ML classifier trained on SOTA indicator signals to predict direction |
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**Current best strategy**: MACD(3,10,3) 4-TF with 2/4 vote majority — **+32.0%/month** (Numba), **+24.3%/month** (verified independent backtest), 0/75 negative months.
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> **This repository contains the research framework.** Trading strategies, broker integrations, and live trading infrastructure are available as separate closed-source modules (`git_ignore_folder/`).
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---
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## Quick Start
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```bash
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# Prerequisites
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conda create -n nexquant python=3.10 -y && conda activate nexquant
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pip install -e .
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# Ensure OHLCV data exists: git_ignore_folder/intraday_pv_all.h5
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# Strategy Discovery Loop (10,000 iterations, ~1 hour)
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python scripts/nexquant_rd_loop.py --iterations 10000
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# Price-Action Indicator Loop (grid search all TA-Lib indicators)
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python scripts/nexquant_priceaction_loop.py
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# Top strategies report
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python nexquant.py best -n 20 -m monthly_return --min-trades 30
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```
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---
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## Strategy Discovery
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### R&D Loop (`scripts/nexquant_rd_loop.py`)
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```
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┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
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│ Explore │ ──→ │ Exploit │ ──→ │ Optuna │ ──→ │ LightGBM │
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│ (Random) │ │ (Mutate) │ │ (Tuning) │ │ (ML) │
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└──────────┘ └──────────┘ └──────────┘ └──────────┘
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30% 70% /500 iter /2000 iter
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```
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**17 TA-Lib indicators**: MACD, RSI, Donchian, SAR, ADX, BBANDS, CCI, WCLPRICE, MFI, OBV, STOCH, ROC, AROON, AROONOSC, MOM, ULTOSC, WILLR
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**4 timeframes**: 15min, 30min, 1h, 4h
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**3 strategy types**: Single-TF, Multi-TF (vote majority), Portfolio (indicator ensemble)
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**Discovery example** (50,000 iterations):
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```
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random → SAR(+65) → MACD(+73) → MACD-mutated(+102.75, +32%/month)
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↓
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Optuna tuned params
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↓
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LightGBM ensemble
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```
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### Grid Search (`scripts/nexquant_priceaction_loop.py`)
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Deterministic parameter grid over all 17 indicators. Finds MACD(3,10,3) as optimal.
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### Portfolio Optimizer (`scripts/nexquant_portfolio_optimizer.py`)
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Greedy correlation-aware selection from discovered strategies.
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---
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## Live Trading
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Closed-source module at `git_ignore_folder/nexquant_live_trader.py`. Architecture:
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```
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MACD(3,10,3) Signal → cTrader OpenAPI → Live Account
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4-TF 2/4 Votes (WebSocket+Protobuf) ↓
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Paper Mode
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```
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Integration: cTrader WebSocket `live.ctraderapi.com:5035`, OAuth2 authentication, Protobuf message encoding, FIX protocol.
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---
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## Features
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### ⚡ Numba Backtest
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- 735M bars/second (0.003s for 2.26M bars)
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- JIT-compiled profit/drawdown/sharpe computation
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- Signal construction via pandas resample + TA-Lib (~0.4s) is the bottleneck
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### 🔍 Four Discovery Methods
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- **Explore**: Random indicator + timeframe + parameters
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- **Exploit**: Mutation of top-5 SOTA strategies (parameter tweak, indicator swap, timeframe change)
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- **Optuna**: 20-trial TPE hyperparameter optimization on best strategy
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- **LightGBM**: ML classifier on SOTA indicator signals (80/20 train/test split)
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### 📊 TA-Lib Integration
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- 17 indicators with full parameter ranges
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- Auto-guard against bad parameters (negative/zero values that crash TA-Lib)
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- Multi-timeframe voting with configurable threshold
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### 🔒 Security & Quality
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- 0 Dependabot alerts, 0 CodeScan alerts
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- No proprietary terms in git history
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- Closed-source detection CI
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---
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## Project Structure
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```
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nexquant/
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├── scripts/ # Strategy discovery & trading
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│ ├── nexquant_rd_loop.py # High-speed R&D loop (Numba + Optuna + ML)
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│ ├── nexquant_priceaction_loop.py # TA-Lib grid search loop
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│ ├── nexquant_portfolio_optimizer.py # Correlation-aware portfolio selection
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│ ├── nexquant_gridsearch.py # Deterministic parameter grid search
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│ ├── nexquant_daily_strategies.py # Daily Kronos + factor combinations
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│ ├── nexquant_gen_strategies_real_bt.py # LLM-based strategy generation
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│ ├── nexquant_autopilot.py # 24/7 continuous generator
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│ └── nexquant_parallel.py # Multi-instance parallel runs
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├── rdagent/ # Core framework (LLM-based, see note below)
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│ ├── app/ # CLI and scenario apps
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│ ├── components/ # Backtest engine, protections, coders
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│ ├── core/ # Core abstractions
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│ ├── scenarios/ # Domain-specific scenarios
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│ └── utils/ # Utilities
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├── git_ignore_folder/ # Closed-source (never committed)
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│ ├── nexquant_live_trader.py # cTrader live trading
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│ ├── nexquant_fix_trader.py # FIX protocol trader
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│ ├── intraday_pv_all.h5 # OHLCV data
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│ ├── gbpusdt_1min.h5 # GBP/USD data
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│ └── btc_1min.h5 # BTC data
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├── test/ # 1,125+ collected tests
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├── data_config.yaml # Walk-forward split configuration
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├── requirements.txt # Dependencies
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└── AGENTS.md # Agent configuration & workflow guide
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```
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> **Note on `rdagent/`**: The LLM-based R&D framework (`rdagent fin_quant`) is part of the codebase but the Qlib/CoSTEER pipeline currently produces zero factors. The primary strategy discovery path is the Numba-based loop in `scripts/`.
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---
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## Installation
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### Prerequisites
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- **Conda** (Miniconda or Anaconda)
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- **TA-Lib** system library (`apt install ta-lib` or `brew install ta-lib`)
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- **Linux** (Ubuntu 22.04+)
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### Install
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```bash
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git clone https://github.com/TPTBusiness/NexQuant && cd NexQuant
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conda create -n nexquant python=3.10 -y && conda activate nexquant
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pip install -e .
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```
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### Data
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Place OHLCV HDF5 data at `git_ignore_folder/intraday_pv_all.h5`:
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```python
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# Format: MultiIndex (datetime, instrument), columns: $open $close $high $low $volume
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df.to_hdf('git_ignore_folder/intraday_pv_all.h5', key='data')
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```
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---
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## License
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**GNU Affero General Public License v3.0 (AGPL-3.0)**. See [`LICENSE`](LICENSE).
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---
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## Disclaimer
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NexQuant is provided for **research and educational purposes only**. Past performance does not guarantee future results. Users assume all liability.
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