Initial commit — Trading AI Secure project complet

Architecture Docker (8 services), FastAPI, TimescaleDB, Redis, Streamlit.
Stratégies : scalping, intraday, swing. MLEngine + RegimeDetector (HMM).
BacktestEngine + WalkForwardAnalyzer + Optuna optimizer.
Routes API complètes dont /optimize async.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Tika
2026-03-08 17:38:09 +00:00
commit da30ef19ed
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# ============================================================
# API - Container trading-api (FastAPI backend)
# ============================================================
# Serveur ASGI
uvicorn[standard]==0.24.0
# Market Data
yfinance>=1.0.0
alpha-vantage==2.3.1
# Technical Analysis (pandas-based, pas de lib C requise)
ta==0.11.0
# Optimisation paramètres
optuna>=4.0.0
# Monitoring
prometheus-client==0.19.0
# Notifications
python-telegram-bot==20.7

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# ============================================================
# BASE - Partagé entre tous les containers
# ============================================================
# Data
numpy==1.26.2
pandas==2.1.3
scipy==1.11.4
# Database
sqlalchemy==2.0.23
psycopg2-binary==2.9.9
alembic==1.13.0
# Cache
redis==5.0.1
# Async
aiohttp==3.9.1
aiofiles==23.2.1
httpx==0.25.2
# HTTP
requests==2.31.0
requests-oauthlib==1.3.1
# Config
python-dotenv==1.0.0
pyyaml==6.0.1
# Date/Time
python-dateutil==2.8.2
pytz==2023.3.post1
# Logging
loguru==0.7.2
python-json-logger==2.0.7
# API Framework (utilisé par api + ml services)
fastapi==0.104.1
pydantic==2.5.0
pydantic-settings==2.1.0

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# ============================================================
# DASHBOARD - Container trading-dashboard (Streamlit UI)
# ============================================================
# UI Framework
streamlit==1.29.0
# Visualisation
plotly==5.18.0
matplotlib==3.8.2
seaborn==0.13.0
# HTTP client pour appels API
httpx==0.25.2
requests==2.31.0

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# ============================================================
# ML - Container trading-ml (Machine Learning engine)
# ============================================================
# Serveur ASGI
uvicorn[standard]==0.24.0
# Machine Learning
scikit-learn==1.3.2
xgboost==2.0.3
lightgbm==4.1.0
hmmlearn==0.3.0
# Optimisation
optuna==3.5.0
# Time Series
statsmodels==0.14.1
# Technical Analysis (feature engineering, pandas-based)
ta==0.11.0
# Market Data (pour entraînement)
yfinance>=1.0.0