autolfow_Bot

This folder holds configuration for an automated trading bot.

Files:

• config.json - Exchange and strategy configuration. Replace YOUR_API_KEY and YOUR_SECRET with real credentials or use environment variables/secrets management.

Security:

• Never commit secrets to version control. Use environment variables or a secrets manager when running the bot.

Quick start (Windows PowerShell):

1. Copy .env.example to .env and fill in your keys, or set the environment variables in your shell:

copy .env.example .env
$env:EXCHANGE_KEY = 'your_api_key_here'
$env:EXCHANGE_SECRET = 'your_api_secret_here'

2. Create a virtual environment and install dependencies:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt

3. Run the smoke test to ensure the strategy imports correctly:

python run_smoke.py

If the import fails, check that freqtrade and the freqai extension (plus TensorFlow) are installed.

FreqAI configuration

The strategy supports optional integration with freqai (an ML add-on). The ML features are disabled by default to avoid requiring heavy ML dependencies. To enable and tune the ML model, add the following to your config.json.

Example (top-level + strategy-scoped overrides):

{
	"freqai_enabled": true,
	"freqai_model_parameters": {
		"epochs": 50,
		"batch_size": 16,
		"optimizer": "adam",
		"loss": "mse",
		"metrics": ["mae"],
		"layers": [
			{"type": "MultiHeadAttention", "num_heads": 4, "key_dim": 32},
			{"type": "Dense", "units": 32, "activation": "relu"},
			{"type": "Dense", "units": 1}
		]
	},
	"strategy_config": {
		"MLGridStrategy": {
			"freqai_enabled": true,
			"freqai_model_parameters": {
				"epochs": 25
			}
		}
	}
}

Notes:

• freqai_enabled: enable ML features (requires freqai and TensorFlow installed).
• freqai_model_parameters: merged with strategy defaults; you can override specific keys (e.g. epochs) at top-level or for the named strategy under strategy_config.
• If you enable freqai but the ML dependencies are missing, the strategy will skip ML features and keep running (it logs the resolved settings on startup).

If you want help selecting model layers or training parameters, tell me your target latency and model complexity and I can suggest conservative defaults.

Bybit testnet and .env (recommended)

1. Create Bybit testnet API keys:

   • Visit https://testnet.bybit.com/ and log in (or create an account).
   • Go to "API Management" and create a new API key for trading; restrict it to "Trade" only (no withdrawals).
   • Copy the API Key and Secret; keep the secret in a secure place.

2. Use a local .env file (never commit this file):

   • Copy .env.example to .env and fill EXCHANGE_KEY and EXCHANGE_SECRET.
   • The provided PowerShell helper scripts (scripts/run_backtest.ps1 and scripts/run_backtest_docker.ps1) will load .env automatically for local runs.

3. Confirm dry-run mode:

   • Ensure "dry_run": true is set in config.json while testing on testnet.

If you prefer system-level environment variables, set EXCHANGE_KEY and EXCHANGE_SECRET in your shell instead of using .env.