https://github.com/tptbusiness/nexquant
NexQuant is an autonomous AI agent for quantitative strategy research. Built on a multi-agent framework
https://github.com/tptbusiness/nexquant
ai-agents algorithmic-trading autonomous-agents backtesting eurusd forex foundation-models ftmo kronos llm multi-agent optuna python pytorch quantitative-finance streamlit trading-bot
Last synced: 13 days ago
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NexQuant is an autonomous AI agent for quantitative strategy research. Built on a multi-agent framework
- Host: GitHub
- URL: https://github.com/tptbusiness/nexquant
- Owner: TPTBusiness
- License: agpl-3.0
- Created: 2026-03-22T19:28:20.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2026-06-01T12:48:18.000Z (14 days ago)
- Last Synced: 2026-06-01T14:23:42.320Z (14 days ago)
- Topics: ai-agents, algorithmic-trading, autonomous-agents, backtesting, eurusd, forex, foundation-models, ftmo, kronos, llm, multi-agent, optuna, python, pytorch, quantitative-finance, streamlit, trading-bot
- Language: Python
- Homepage: https://github.com/TPTBusiness/NexQuant
- Size: 147 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
- Support: SUPPORT.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
# NexQuant
High-Speed Strategy Discovery Framework
Quick Start •
Strategy Discovery •
Live Trading •
Features
---
## Overview
**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:
| Method | Frequency | Description |
|--------|-----------|-------------|
| **Explore** | 30% of iterations | Random strategies from 17 TA-Lib indicators across timeframes |
| **Exploit** | 70% of iterations | Mutate the best-known strategy (change params, indicator, or timeframe) |
| **Optuna** | Every 500 iterations | 20-trial hyperparameter optimization on the current best |
| **LightGBM** | Every 2000 iterations | ML classifier trained on SOTA indicator signals to predict direction |
**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.
> **This repository contains the research framework.** Trading strategies, broker integrations, and live trading infrastructure are available as separate closed-source modules (`git_ignore_folder/`).
---
## Quick Start
```bash
# Prerequisites
conda create -n nexquant python=3.10 -y && conda activate nexquant
pip install -e .
# Ensure OHLCV data exists: git_ignore_folder/intraday_pv_all.h5
# Strategy Discovery Loop (10,000 iterations, ~1 hour)
python scripts/nexquant_rd_loop.py --iterations 10000
# Price-Action Indicator Loop (grid search all TA-Lib indicators)
python scripts/nexquant_priceaction_loop.py
# Top strategies report
python nexquant.py best -n 20 -m monthly_return --min-trades 30
```
---
## Strategy Discovery
### R&D Loop (`scripts/nexquant_rd_loop.py`)
```
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Explore │ ──→ │ Exploit │ ──→ │ Optuna │ ──→ │ LightGBM │
│ (Random) │ │ (Mutate) │ │ (Tuning) │ │ (ML) │
└──────────┘ └──────────┘ └──────────┘ └──────────┘
30% 70% /500 iter /2000 iter
```
**17 TA-Lib indicators**: MACD, RSI, Donchian, SAR, ADX, BBANDS, CCI, WCLPRICE, MFI, OBV, STOCH, ROC, AROON, AROONOSC, MOM, ULTOSC, WILLR
**4 timeframes**: 15min, 30min, 1h, 4h
**3 strategy types**: Single-TF, Multi-TF (vote majority), Portfolio (indicator ensemble)
**Discovery example** (50,000 iterations):
```
random → SAR(+65) → MACD(+73) → MACD-mutated(+102.75, +32%/month)
↓
Optuna tuned params
↓
LightGBM ensemble
```
### Grid Search (`scripts/nexquant_priceaction_loop.py`)
Deterministic parameter grid over all 17 indicators. Finds MACD(3,10,3) as optimal.
### Portfolio Optimizer (`scripts/nexquant_portfolio_optimizer.py`)
Greedy correlation-aware selection from discovered strategies.
---
## Live Trading
Closed-source module at `git_ignore_folder/nexquant_live_trader.py`. Architecture:
```
MACD(3,10,3) Signal → cTrader OpenAPI → Live Account
4-TF 2/4 Votes (WebSocket+Protobuf) ↓
Paper Mode
```
Integration: cTrader WebSocket `live.ctraderapi.com:5035`, OAuth2 authentication, Protobuf message encoding, FIX protocol.
---
## Features
### ⚡ Numba Backtest
- 735M bars/second (0.003s for 2.26M bars)
- JIT-compiled profit/drawdown/sharpe computation
- Signal construction via pandas resample + TA-Lib (~0.4s) is the bottleneck
### 🔍 Four Discovery Methods
- **Explore**: Random indicator + timeframe + parameters
- **Exploit**: Mutation of top-5 SOTA strategies (parameter tweak, indicator swap, timeframe change)
- **Optuna**: 20-trial TPE hyperparameter optimization on best strategy
- **LightGBM**: ML classifier on SOTA indicator signals (80/20 train/test split)
### 📊 TA-Lib Integration
- 17 indicators with full parameter ranges
- Auto-guard against bad parameters (negative/zero values that crash TA-Lib)
- Multi-timeframe voting with configurable threshold
### 🔒 Security & Quality
- 0 Dependabot alerts, 0 CodeScan alerts
- No proprietary terms in git history
- Closed-source detection CI
---
## Project Structure
```
nexquant/
├── scripts/ # Strategy discovery & trading
│ ├── nexquant_rd_loop.py # High-speed R&D loop (Numba + Optuna + ML)
│ ├── nexquant_priceaction_loop.py # TA-Lib grid search loop
│ ├── nexquant_portfolio_optimizer.py # Correlation-aware portfolio selection
│ ├── nexquant_gridsearch.py # Deterministic parameter grid search
│ ├── nexquant_daily_strategies.py # Daily Kronos + factor combinations
│ ├── nexquant_gen_strategies_real_bt.py # LLM-based strategy generation
│ ├── nexquant_autopilot.py # 24/7 continuous generator
│ └── nexquant_parallel.py # Multi-instance parallel runs
├── rdagent/ # Core framework (LLM-based, see note below)
│ ├── app/ # CLI and scenario apps
│ ├── components/ # Backtest engine, protections, coders
│ ├── core/ # Core abstractions
│ ├── scenarios/ # Domain-specific scenarios
│ └── utils/ # Utilities
├── git_ignore_folder/ # Closed-source (never committed)
│ ├── nexquant_live_trader.py # cTrader live trading
│ ├── nexquant_fix_trader.py # FIX protocol trader
│ ├── intraday_pv_all.h5 # OHLCV data
│ ├── gbpusdt_1min.h5 # GBP/USD data
│ └── btc_1min.h5 # BTC data
├── test/ # 1,125+ collected tests
├── data_config.yaml # Walk-forward split configuration
├── requirements.txt # Dependencies
└── AGENTS.md # Agent configuration & workflow guide
```
> **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/`.
---
## Installation
### Prerequisites
- **Conda** (Miniconda or Anaconda)
- **TA-Lib** system library (`apt install ta-lib` or `brew install ta-lib`)
- **Linux** (Ubuntu 22.04+)
### Install
```bash
git clone https://github.com/TPTBusiness/NexQuant && cd NexQuant
conda create -n nexquant python=3.10 -y && conda activate nexquant
pip install -e .
```
### Data
Place OHLCV HDF5 data at `git_ignore_folder/intraday_pv_all.h5`:
```python
# Format: MultiIndex (datetime, instrument), columns: $open $close $high $low $volume
df.to_hdf('git_ignore_folder/intraday_pv_all.h5', key='data')
```
---
## License
**GNU Affero General Public License v3.0 (AGPL-3.0)**. See [`LICENSE`](LICENSE).
---
## Disclaimer
NexQuant is provided for **research and educational purposes only**. Past performance does not guarantee future results. Users assume all liability.