# 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.
## Quick Start
# 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
## Installation
### Prerequisites
- **Conda** (Miniconda or Anaconda)
- **TA-Lib** system library (`apt install ta-lib` or `brew install ta-lib`)
- **Linux** (Ubuntu 22.04+)
### Install
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`:
# 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.