gavindiaz/NexQuant
1
0
Fork 0
mirror of https://github.com/NicolasBohn/NexQuant.git synced 2026-07-27 23:47:46 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: fix duplicate sections, add hardware requirements and data setup guide

Browse Source 
- Remove duplicate Configuration and CLI Commands sections
- Add System Requirements table (GPU VRAM, RAM, CUDA)
- Expand Data Setup with concrete step-by-step instructions
- Add prerequisites checklist to Quick Start (Docker, data, LLM health)
- Consolidate all CLI commands into one section

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
TPTBusiness
2026-04-18 09:12:58 +02:00
parent 96e4f49740
commit 38cf4bc63e
1 changed files with 148 additions and 208 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+148 -208
View File
@@ -101,11 +101,24 @@ All code in Predix is originally written and implemented independently. Predix e
## Installation
### System Requirements
| Component | Minimum | Recommended |
|-----------|---------|-------------|
| **GPU VRAM** | 8 GB | 16 GB (RTX 4080 / 5060 Ti) |
| **RAM** | 16 GB | 32 GB |
| **Storage** | 20 GB | 50 GB (models + data) |
| **OS** | Linux (Ubuntu 22.04+) | Linux |
| **CUDA** | 12.0+ | 12.4+ |
> Local LLMs require a CUDA-capable GPU. The default model (Qwen3.6-35B Q3) uses ~13.6 GB VRAM. CPU-only inference is possible but very slow (not recommended for production use).
### Prerequisites
- **Conda** (Miniconda or Anaconda) - Required for environment management
- **Docker** (required for sandboxed code execution)
- **Linux** (officially supported; macOS/Windows may work with adjustments)
- **Conda** (Miniconda or Anaconda) — required for environment management
- **Docker** — required for sandboxed factor/model code execution (`docker run hello-world` to verify)
- **llama.cpp** — for local LLM inference (see [llama.cpp build guide](https://github.com/ggml-org/llama.cpp))
- **Linux** — officially supported; macOS/Windows may work with adjustments
### Quick Install
@@ -120,14 +133,71 @@ conda activate predix
# Install in editable mode
pip install -e .
# Verify Docker is accessible
docker run --rm hello-world
```
> **Important:** Predix requires a conda environment to manage dependencies properly.
> Using plain Python or other environment managers may cause conflicts.
### Configuration
---
## Data Setup
Predix requires **1-minute EUR/USD OHLCV data** in HDF5 format. This is a hard prerequisite — the system cannot run without it.
### Step 1: Get the data
Download 1-minute EUR/USD data (2020–present) from any of these free sources:
| Source | Cost | Notes |
|--------|------|-------|
| **[Dukascopy](https://www.dukascopy.com/swiss/english/marketfeed/historical/)** | Free | Best quality free EUR/USD tick data |
| **[OANDA API](https://developer.oanda.com/)** | Free (demo) | Requires API key, programmatic access |
| **[TrueFX](https://truefx.com/)** | Free | Institutional-quality tick data |
| **[Kaggle](https://www.kaggle.com/datasets?search=EURUSD+1min)** | Free | Search "EURUSD 1 minute" |
| **MetaTrader 5** | Free | Export via `copy_rates_range()` |
### Step 2: Convert to HDF5
```python
import pandas as pd
df = pd.read_csv('eurusd_1min.csv', parse_dates=['datetime'])
df = df.rename(columns={'open': '$open', 'close': '$close',
'high': '$high', 'low': '$low', 'volume': '$volume'})
df['instrument'] = 'EURUSD'
df = df.set_index(['datetime', 'instrument'])
for col in ['$open', '$close', '$high', '$low', '$volume']:
df[col] = df[col].astype('float32')
import os
os.makedirs('git_ignore_folder/factor_implementation_source_data', exist_ok=True)
df.to_hdf('git_ignore_folder/factor_implementation_source_data/intraday_pv.h5', key='data', mode='w')
```
### Required HDF5 format
| Field | Type | Description |
|-------|------|-------------|
| **Index** | MultiIndex `(datetime, instrument)` | Timestamp + currency pair |
| **`$open`** | float32 | Open price |
| **`$close`** | float32 | Close price |
| **`$high`** | float32 | High price |
| **`$low`** | float32 | Low price |
| **`$volume`** | float32 | Tick volume |
**Save location:** `git_ignore_folder/factor_implementation_source_data/intraday_pv.h5`
---
## Configuration
### Environment Setup
Create a `.env` file in the project root:
1. **Create `.env` file:**
```bash
# Local LLM (llama.cpp)
OPENAI_API_KEY=local
@@ -143,7 +213,8 @@ EMBEDDING_MODEL=nomic-embed-text
QLIB_DATA_DIR=~/.qlib/qlib_data/eurusd_1min_data
```
2. **Start LLM server (llama.cpp):**
### LLM Server (llama.cpp)
```bash
~/llama.cpp/build/bin/llama-server \
--model ~/models/qwen3.6/Qwen3.6-35B-A3B-UD-Q3_K_XL.gguf \
@@ -158,56 +229,74 @@ QLIB_DATA_DIR=~/.qlib/qlib_data/eurusd_1min_data
--reasoning off
```
> **Important flags and token budget:**
> - `--ctx-size 240000 --parallel 2` — allocates **2 slots × 120,000 tokens each**. `fin_quant` prompts can reach 80k+ tokens with full factor history; a smaller slot causes silent overflow and empty/invalid responses.
>
> **Token budget breakdown per fin_quant request:**
> | Component | Approx. tokens |
> |---|---|
> | System prompt + scenario description | ~3,000 |
> | `MAX_FACTOR_HISTORY=5` past experiments × ~2,500 | ~12,500 |
> | RAG context + instructions | ~2,000 |
> | **Total** | **~17,500** (well within 120k slot) |
>
> Formula: `ctx_size / parallel` must satisfy `n_ctx_slot > MAX_FACTOR_HISTORY × 2500 + 5000`.
>
> - `--reasoning off` — **critical**: completely disables Qwen3 chain-of-thought. `--reasoning-budget 0` is not sufficient — it still starts and immediately aborts reasoning, producing empty JSON responses. Only `--reasoning off` prevents this entirely.
> **Important flags:**
> - `--ctx-size 240000 --parallel 2` — allocates **2 slots × 120,000 tokens each**. `fin_quant` prompts can reach 80k+ tokens with full factor history; a smaller slot causes silent overflow and empty responses.
> - `--reasoning off` — **critical**: completely disables Qwen3 chain-of-thought. `--reasoning-budget 0` is not sufficient and produces empty JSON responses.
> - `--n-gpu-layers 24` — 4 fewer than maximum on RTX 5060 Ti (16 GB), freeing ~500 MB VRAM for the larger KV cache.
> - `-ctk q4_0 -ctv q4_0` — quantises the KV cache to 4-bit, reducing VRAM from ~5 GB to ~1.3 GB at 240k context.
### Data Configuration
Edit [`data_config.yaml`](data_config.yaml) to customize walk-forward splits:
```yaml
instrument: EURUSD
frequency: 1min
data_path: ~/.qlib/qlib_data/eurusd_1min_data
train_start: "2022-03-14"
train_end: "2024-06-30"
valid_start: "2024-07-01"
valid_end: "2024-12-31"
test_start: "2025-01-01"
test_end: "2026-03-20"
market_context:
spread_bps: 1.5
target_arr: 9.62
max_drawdown: 20
```
---
## Quick Start
### Prerequisites checklist
```bash
# 1. Docker running?
docker run --rm hello-world
# 2. Data in place?
ls git_ignore_folder/factor_implementation_source_data/intraday_pv.h5
# 3. LLM server running?
curl http://localhost:8081/health
```
### 1. Run Trading Loop
```bash
# Activate conda environment
conda activate predix
# Start EURUSD trading loop
rdagent fin_quant
# With options
# or with explicit options:
rdagent fin_quant --loop-n 5 --step-n 2
```
### 2. Monitor Results
```bash
# Start the UI dashboard
# Web dashboard
rdagent server_ui --port 19899 --log-dir git_ignore_folder/RD-Agent_workspace/
# then open http://127.0.0.1:19899
# Or open in browser
# http://127.0.0.1:19899
# Best strategies so far
python predix.py best
```
### 3. Loop Continuously
To run the trading loop continuously with auto-restart:
### 3. Run Continuously
```bash
# Simple loop
while true; do
rdagent fin_quant
sleep 5
@@ -218,39 +307,26 @@ done
## CLI Commands
### Trading Loop
### Factor & Strategy Loop
| Command | Description |
|---------|-------------|
| `rdagent fin_quant` | Start factor evolution loop |
| `rdagent fin_quant --loop-n 5` | Run 5 evolution loops |
| `rdagent fin_quant` | Start autonomous factor + model evolution loop |
| `rdagent fin_quant --loop-n 5` | Run exactly 5 evolution loops |
| `rdagent fin_quant --with-dashboard` | Start with web dashboard |
| `rdagent fin_quant --cli-dashboard` | Start with CLI Rich dashboard |
### Parallel Execution
| Command | Description |
|---------|-------------|
| `python predix_parallel.py --runs 5 --api-keys 1 -m openrouter` | Run 5 parallel factor evolutions |
| `python predix_parallel.py --runs 20 --api-keys 2 -m openrouter` | Run 20 runs with 2 API keys |
### AI Strategy Generation (with REAL OHLCV Backtest)
| Command | Description |
|---------|-------------|
| `python predix_gen_strategies_real_bt.py` | Generate 10 strategies with LLM + real backtest |
| `python predix_gen_strategies_real_bt.py 20` | Generate 20 strategies |
| `python predix_gen_strategies_real_bt.py 5` | Generate 5 strategies (faster) |
| `rdagent fin_factor` | Factor-only evolution |
| `rdagent fin_model` | Model-only evolution |
### Strategy Reports
| Command | Description |
|---------|-------------|
| `python predix.py best` | Show top strategies by composite score (Sharpe × DD × trade penalty) |
| `python predix.py best` | Show top strategies by composite score |
| `python predix.py best -n 20 -m sharpe` | Top 20 by Sharpe ratio |
| `python predix.py best --show NAME` | Full metadata for one strategy |
| `python predix_strategy_report.py` | Generate reports for ALL strategies |
| `python predix_strategy_report.py results/strategies_new/123_MyStrategy.json` | Report for single strategy |
| `python predix_gen_strategies_real_bt.py` | Generate 10 strategies with LLM + real backtest |
| `python predix_gen_strategies_real_bt.py 20` | Generate 20 strategies |
### Factor Evaluation
@@ -260,62 +336,21 @@ done
| `python predix.py top -n 20` | Show top 20 factors by IC |
| `python predix.py portfolio-simple` | Simple portfolio optimization |
### Other Utilities
### Parallel Execution
| Command | Description |
|---------|-------------|
| `python predix_parallel.py --runs 5 --api-keys 1 -m openrouter` | Run 5 parallel factor evolutions |
| `python predix_parallel.py --runs 20 --api-keys 2 -m openrouter` | Run 20 runs with 2 API keys |
### Monitoring & Debug
| Command | Description |
|---------|-------------|
| `rdagent server_ui --port 19899 --log-dir <path>` | Start web dashboard |
| `rdagent health_check` | Validate environment setup |
| `python predix_batch_backtest.py` | Batch backtest multiple factors |
| `python predix_parallel.py` | Parallel factor evolution |
| `python predix_rebacktest_strategies.py` | Re-backtest existing strategies |
| `python debug_backtest.py` | Debug backtest alignment & IC |
### Environment Options
| Env Variable | Description | Example |
|--------------|-------------|---------|
| `OPENROUTER_API_KEY` | OpenRouter API key | `sk-or-v1-...` |
| `OPENAI_API_KEY` | Alternative: OpenAI key | `sk-...` |
| `CHAT_MODEL` | LLM model | `openrouter/qwen/qwen3.6-plus:free` |
| `OPENROUTER_MODEL` | Specific OpenRouter model | `openrouter/qwen/qwen3.6-plus:free` |
| `NO_COLOR` | Disable ANSI colors | `1` |
---
## Configuration
### Data Configuration
Edit [`data_config.yaml`](data_config.yaml) to customize:
```yaml
instrument: EURUSD
frequency: 1min
data_path: ~/.qlib/qlib_data/eurusd_1min_data
# Walk-forward split
train_start: "2022-03-14"
train_end: "2024-06-30"
valid_start: "2024-07-01"
valid_end: "2024-12-31"
test_start: "2025-01-01"
test_end: "2026-03-20"
# Market context for LLM prompts
market_context:
spread_bps: 1.5
target_arr: 9.62 # Target annual return (%)
max_drawdown: 20 # Max drawdown (%)
```
### Environment Variables
| Variable | Description | Example |
|----------|-------------|---------|
| `CHAT_MODEL` | LLM for reasoning | `gpt-4o`, `deepseek-chat` |
| `EMBEDDING_MODEL` | Embedding model | `text-embedding-3-small` |
| `OPENAI_API_KEY` | API key for OpenAI | `sk-...` |
| `DEEPSEEK_API_KEY` | API key for DeepSeek | `sk-...` |
| `DS_LOCAL_DATA_PATH` | Local data directory | `./data` |
---
@@ -367,9 +402,9 @@ Real-time dashboard for monitoring:
Automated quality assurance:
- **60 Integration Tests** - All features tested automatically
- **Bandit Security Scanner** - Pre-commit security checks
- **Pre-commit Hooks** - Tests run before EVERY commit
- **60 Integration Tests** — all features tested automatically on every commit
- **Bandit Security Scanner** — pre-commit security checks
- **Weekly Dependency Audit** — automated vulnerability scan via GitHub Actions
---
@@ -382,118 +417,23 @@ predix/
│ ├── components/ # Reusable agent components
│ │ ├── backtesting/ # Backtest engine & protections
│ │ │ ├── backtest_engine.py
│ │ │ ├── vbt_backtest.py # Unified backtest engine
│ │ │ ├── results_db.py
│ │ │ ├── risk_management.py
│ │ │ └── protections/ # Trading protection system (NEW)
│ │ │ ├── base.py
│ │ │ ├── max_drawdown.py
│ │ │ ├── cooldown.py
│ │ │ ├── stoploss_guard.py
│ │ │ ├── low_performance.py
│ │ │ └── protection_manager.py
│ │ ├── coder/ # Factor & model coding
│ │ └── loader.py # Prompt & model loaders
│ │ │ └── protections/ # Trading protection system
│ │ └── coder/ # Factor & model coding (CoSTEER + Optuna)
│ ├── core/ # Core abstractions
│ ├── scenarios/ # Domain-specific scenarios
│ └── utils/ # Utilities
├── test/ # Test suite
│ ├── integration/ # Integration tests (60 tests)
│ │ └── test_all_features.py
│ └── backtesting/ # Unit tests
│ └── test_protections.py
├── constraints/ # Constraint definitions
├── docs/ # Documentation
├── test/ # Test suite (134 tests)
│ └── backtesting/ # Backtest unit tests
├── web/ # Web UI frontend
├── data_config.yaml # Data configuration
├── data_config.yaml # Walk-forward split configuration
├── pyproject.toml # Project metadata
└── requirements.txt # Dependencies
```
---
## Data Setup
Predix requires **1-minute EUR/USD OHLCV data** in HDF5 format.
### Required Format
The data file must be saved as `intraday_pv.h5` with the following structure:
| Field | Type | Description |
|-------|------|-------------|
| **Index** | MultiIndex `(datetime, instrument)` | Timestamp + currency pair |
| **`$open`** | float32 | Open price |
| **`$close`** | float32 | Close price |
| **`$high`** | float32 | High price |
| **`$low`** | float32 | Low price |
| **`$volume`** | float32 | Tick volume |
**Save location:** `git_ignore_folder/factor_implementation_source_data/intraday_pv.h5`
### Where to Get Data
| Source | Cost | Notes |
|--------|------|-------|
| **[Dukascopy](https://www.dukascopy.com/swiss/english/marketfeed/historical/)** | Free | Best free EUR/USD tick data |
| **[OANDA API](https://developer.oanda.com/)** | Free (demo) | Requires API key |
| **[TrueFX](https://truefx.com/)** | Free | Institutional-quality data |
| **[Kaggle](https://www.kaggle.com/datasets?search=EURUSD+1min)** | Free | Search "EURUSD 1 minute" |
| **MetaTrader 5** | Free | Export via `copy_rates_range()` |
### Quick CSV Conversion
```python
import pandas as pd
df = pd.read_csv('eurusd_1min.csv', parse_dates=['datetime'])
df = df.rename(columns={'open': '$open', 'close': '$close',
'high': '$high', 'low': '$low', 'volume': '$volume'})
df['instrument'] = 'EURUSD'
df = df.set_index(['datetime', 'instrument'])
for col in ['$open', '$close', '$high', '$low', '$volume']:
df[col] = df[col].astype('float32')
df.to_hdf('intraday_pv.h5', key='data', mode='w')
```
Expected data columns: `$open`, `$close`, `$high`, `$low`, `$volume`
---
## CLI Commands
| Command | Description |
|---------|-------------|
| `rdagent fin_quant` | Full factor & model co-evolution |
| `rdagent fin_factor` | Factor-only evolution |
| `rdagent fin_model` | Model-only evolution |
| `rdagent fin_factor_report --report-folder=<path>` | Extract factors from financial reports |
| `rdagent general_model <paper-url>` | Extract model from research paper |
| `rdagent rl_trading --mode train --algorithm PPO` | Train RL trading agent |
| `rdagent rl_trading --mode backtest --model-path <path>` | Backtest with trained RL model |
| `rdagent data_science --competition <name>` | Kaggle/data science competition mode |
| `rdagent ui --port 19899 --log-dir <path>` | Start monitoring dashboard |
| `rdagent health_check` | Validate environment setup |
### RL Trading Examples
```bash
# Train new RL agent with PPO
rdagent rl_trading --mode train --algorithm PPO --total-timesteps 100000
# Backtest with trained model
rdagent rl_trading --mode backtest --model-path models/rl_trader.zip
# Disable trading protections (not recommended)
rdagent rl_trading --mode backtest --no-with-protections
# Get help
rdagent rl_trading --help
```
**Note:** RL Trading works without `stable-baselines3` (uses simple fallback strategy). For full RL features, install: `pip install -r requirements/rl.txt`
---
## Requirements
Core dependencies (see [`requirements.txt`](requirements.txt) for full list):