42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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import backtrader as bt
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import torch
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import numpy as np
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import pandas as pd
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from ml.models.forex_mlp import ForexMLP
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class PyTorchAIModel(bt.Strategy):
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def __init__(self):
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self.model = ForexMLP()
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self.model.load_state_dict(torch.load("ml/models/forex_mlp.pt", map_location=torch.device("cpu")))
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self.model.eval()
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self.buy_threshold = 0.7
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self.sell_threshold = 0.6
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def next(self):
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# Skip early bars (for indicators if you add them)
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if len(self.datas[0]) < 30:
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return
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# Create feature vector for the current candle
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features = np.array([[
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self.data.open[0],
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self.data.high[0],
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self.data.low[0],
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self.data.close[0],
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self.data.volume[0]
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]], dtype=np.float32)
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inputs = torch.tensor(features)
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with torch.no_grad():
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output = self.model(inputs)
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buy_score, sell_score = output[0].numpy()
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print(f"[AI] Buy: {buy_score:.3f}, Sell: {sell_score:.3f}")
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# Trade logic
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if buy_score > self.buy_threshold and not self.position:
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self.buy()
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elif sell_score > self.sell_threshold and self.position:
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self.close()
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