Add daily model training scripts and terminal UI for live trading

- Introduced `train_daily.sh` for automating daily model retraining, including data download and model training steps.
- Added `install_cron.sh` for setting up a cron job to run the daily training script.
- Created `setup_schedule.sh` for configuring Systemd timers for daily training tasks.
- Implemented a terminal UI using Rich for real-time monitoring of trading performance, including metrics display and log handling.
- Updated `pyproject.toml` to include the `rich` dependency for UI functionality.
- Enhanced `.gitignore` to exclude model and log files.
- Added database support for trade persistence and metrics calculation.
- Updated README with installation and usage instructions for the new features.

train_daily.sh

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+#!/bin/bash
+# Daily ML Model Training Script
+# 
+# Downloads fresh data and retrains the regime detection model.
+# Can be run manually or scheduled via cron.
+#
+# Usage:
+#   ./train_daily.sh           # Full workflow
+#   ./train_daily.sh --skip-research  # Skip research validation
+
+set -e  # Exit on error
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+LOG_DIR="logs"
+mkdir -p "$LOG_DIR"
+
+TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
+echo "[$TIMESTAMP] Starting daily training..."
+
+# 1. Download fresh data
+echo "Downloading BTC-USDT 1h data..."
+uv run python main.py download -p BTC-USDT -t 1h
+
+echo "Downloading ETH-USDT 1h data..."
+uv run python main.py download -p ETH-USDT -t 1h
+
+# 2. Research optimization (find best horizon)
+echo "Running research optimization..."
+uv run python research/regime_detection.py --output-horizon data/optimal_horizon.txt
+
+# 3. Read best horizon
+if [[ -f "data/optimal_horizon.txt" ]]; then
+    BEST_HORIZON=$(cat data/optimal_horizon.txt)
+    echo "Found optimal horizon: ${BEST_HORIZON} bars"
+else
+    BEST_HORIZON=102
+    echo "Warning: Could not find optimal horizon file. Using default: ${BEST_HORIZON}"
+fi
+
+# 4. Train model
+echo "Training ML model with horizon ${BEST_HORIZON}..."
+uv run python train_model.py --horizon "$BEST_HORIZON"
+
+# 5. Cleanup
+rm -f data/optimal_horizon.txt
+
+TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
+echo "[$TIMESTAMP] Daily training complete."