thats_my_quant V0.1

A crypto and equities charting platform with AI-assisted backtesting.

Quick Start

# 0. (Optionally) Initiate a venv in python 3.11
py -3.11 -m venv .venv
.\.venv\Scripts\activate
python -m pip install -U pip

# 1. Install dependencies
pip install -e .\core
pip install -e ".\backend[dev]"
pip install -e .\mcp-server
cd frontend && npm install && cd ..

# 2. Configure API keys (optional but recommended)
cp .env.example .env
# Edit .env with your API keys

# 3. Run everything
tmq

Open http://localhost:5173 in your browser.

Prerequisites

• Node.js 18+ (for frontend)
• Python 3.10+ (for backend)

Installation

1. Core Library

cd core
pip install -e .

2. Backend

cd backend
pip install -e .

3. Frontend

cd frontend
npm install

Configuration

API Keys (.env)

Create a .env file in the project root with your API keys:

# AI Providers (at least one recommended)
ANTHROPIC_API_KEY=sk-ant-...      # Anthropic (Claude)
OPENROUTER_API_KEY=sk-or-v1-...   # OpenRouter (access to all models)
OPENAI_API_KEY=sk-...             # OpenAI (GPT)
GOOGLE_API_KEY=...                # Google (Gemini)
XAI_API_KEY=...                   # xAI (Grok)

# Data Providers (optional)
ALPACA_API_KEY=...
ALPACA_SECRET_KEY=...
ALPACA_API_ENDPOINT=https://paper-api.alpaca.markets/v2

When you run the app, configured providers will appear in Settings with a checkmark. You can select which provider to use, or enter a custom API key.

Running the App

Option A: CLI (recommended)

tmq

This starts both backend (port 8000) and frontend (port 5173), automatically loading .env.

Option B: Manual (two terminals)

Terminal 1 - Backend:

cd backend
uvicorn tmq_backend.main:app --reload

Terminal 2 - Frontend:

cd frontend
npm run dev

Providers

• AI: OpenAI, Anthropic, Google, xAI, OpenRouter (unified access)
• Crypto Data: Coinbase, Binance, Kraken, Bybit, OKX (API-key free)
• Equity Data: Yahoo Finance (free), Alpaca, Polygon

Features

• Multi-chart view: View 1, 2, or 4 charts simultaneously
• Crypto & Equities: Supports BTC, ETH, stocks (AAPL, MSFT, etc.)
• Technical indicators: SMA, EMA, RSI, Bollinger Bands, etc.
• AI Chat: Ask questions about strategies (requires API key in settings)
• Custom backtesting: Write Python strategies in the code panel

Usage

1. Change symbol: Click the ticker dropdown (top-left of chart)
2. Add indicators: Click "Indicators" button
3. Multi-chart: Use the layout buttons (top-right of chart area)
4. Backtest: Open the CODE panel, write a strategy, click Run
5. Settings: Click the gear icon to configure AI provider and data sources

Code Panel

The code panel lets you write custom trading strategies:

def generate_signals(df):
    """
    df has columns: date, open, high, low, close, volume
    Return (entries, exits) as boolean Series.
    """
    fast = df['close'].rolling(10).mean()
    slow = df['close'].rolling(30).mean()
    entries = (fast > slow) & (fast.shift(1) <= slow.shift(1))
    exits = (fast < slow) & (fast.shift(1) >= slow.shift(1))
    return entries.fillna(False), exits.fillna(False)

Feature Requests

Type /request <your idea> in the code panel and hit Run to submit feedback.

License

MIT