7e4e9315c2
- Added MLFilter class to load and evaluate LightGBM model for trading signals. - Introduced retraining mechanism to update the model daily based on new data. - Created feature engineering and label building utilities for model training. - Updated bot logic to incorporate ML filter for signal validation. - Added scripts for data fetching and model training. Made-with: Cursor
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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import pandas as pd
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import numpy as np
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import os
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from src.bot import TradingBot
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from src.config import Config
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@pytest.fixture
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def config():
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os.environ.update({
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"BINANCE_API_KEY": "k",
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"BINANCE_API_SECRET": "s",
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"SYMBOL": "XRPUSDT",
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"LEVERAGE": "10",
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"RISK_PER_TRADE": "0.02",
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"NOTION_TOKEN": "secret_test",
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"NOTION_DATABASE_ID": "db_test",
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})
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return Config()
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@pytest.fixture
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def sample_df():
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np.random.seed(0)
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n = 100
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close = np.cumsum(np.random.randn(n) * 0.01) + 0.5
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return pd.DataFrame({
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"open": close,
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"high": close * 1.005,
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"low": close * 0.995,
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"close": close,
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"volume": np.random.randint(100000, 1000000, n).astype(float),
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})
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@pytest.mark.asyncio
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async def test_bot_processes_signal(config, sample_df):
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with patch("src.bot.BinanceFuturesClient") as MockExchange:
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MockExchange.return_value = AsyncMock()
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bot = TradingBot(config)
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bot.exchange = AsyncMock()
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bot.exchange.get_balance = AsyncMock(return_value=1000.0)
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bot.exchange.get_position = AsyncMock(return_value=None)
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bot.exchange.place_order = AsyncMock(return_value={"orderId": "123"})
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bot.exchange.set_leverage = AsyncMock(return_value={})
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bot.exchange.calculate_quantity = MagicMock(return_value=100.0)
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bot.exchange.MIN_NOTIONAL = 5.0
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with patch("src.bot.Indicators") as MockInd:
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mock_ind = MagicMock()
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mock_ind.calculate_all.return_value = sample_df
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mock_ind.get_signal.return_value = "LONG"
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mock_ind.get_atr_stop.return_value = (0.48, 0.56)
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MockInd.return_value = mock_ind
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await bot.process_candle(sample_df)
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