🧠 BTC Signal Bot

Streamlit-powered command center for watching Bitcoin price action, producing RSI/MACD-informed trade calls, and auditing simulated performance without leaving your browser.

✨ Features

• Market data ingestion — fetches BTC-USD candles from Yahoo Finance, then reshapes them into 4h, weekly, or monthly frames automatically.
• Signal engine — couples RSI crossover triggers with EMA trend gating, MACD confirmation, and divergence annotations.
• Backtesting — simulates long-only execution with fees and slippage baked in, reporting win rate, drawdown, exposure, and every trade.
• Interactive UI — Plotly overlays, downloadable CSV output, and an auto-refresh toggle keep dashboards current.
• Automated tests — unit suites cover the strategy core and backtester to guard against regressions.

🚀 Quick start

git clone https://github.com/angeloudebedout/BTCSignalBot.git
cd BTCSignalBot

# Create a Python environment (recommended)
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

# Run unit tests
python -m unittest discover -s Tests

# Launch the dashboard
streamlit run streamlit_app.py

The Streamlit board boots into 4h candles with auto-refresh enabled. Adjust indicator knobs in the sidebar and flip on overlays for MACD, RSI, EMAs, Bollinger Bands, divergence markers, or backtest trades.

📦 Package usage

from signalbot.main import run

df = run(interval="1h", oversold=25, overbought=75, lookback_days=120)
print(df.tail()[["close", "rsi", "signal", "signal_strength"]])

signalbot.backtest.backtest_signals(df) yields (metrics, trades_df, equity_curve), letting you slot the simulated track record into other workflows.

☁️ Deploying to Streamlit Cloud

1. Push the repository to GitHub.
2. On share.streamlit.io, create a new app pointing to streamlit_app.py.
3. Use python -m venv .venv && pip install -r requirements.txt for the build step (or lean on Streamlit Cloud's cached pip env).
4. Surface streamlit_app.py as the entry point—auto-refresh is already wired up.

🛠️ Project structure

signalbot/
  data.py        # Yahoo Finance ingestion helpers
  indicators.py  # RSI, EMA, MACD, Bollinger, candlestick patterns
  strategy.py    # Signal generation and divergence detection
  backtest.py    # Fee-aware performance simulation
  plotting.py    # Plotly figure factories used by the UI
streamlit_app.py # Streamlit dashboard
Tests/           # Unit tests for strategy and backtester

🤝 Contributing

Issues and PRs are welcome. Please:

• Keep the test suite green (python -m unittest discover -s Tests)
• Run python -m compileall streamlit_app.py signalbot Tests to catch syntax errors
• Use descriptive commit messages so deployment diffs stay clear

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Maintained by Angelou deBedout. If you ship improvements or deploy your own dashboard, feel free to share! 🚀

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🧩 Dependencies & Setup

This project was developed and tested with:

Library	Version
Python	3.10+
streamlit	1.39.0
streamlit-autorefresh	1.0.1
plotly	5.24.1
pandas	2.2.3
numpy	1.26.4
matplotlib	3.9.2
requests	2.32.3
openpyxl	3.1.5
yfinance	0.2.43
pytest	8.3.3

Quick setup

python3 -m venv venv
source venv/bin/activate        # or venv\Scripts\activate on Windows
pip install -r requirements.txt
streamlit run streamlit_app.py