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@@ -34,7 +34,6 @@ def on_message_trade(_ws, message):
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trade_data.append(trade)
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check_and_train_model()
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predict_market_direction()
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- show_message()
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def on_message_depth(_ws, message):
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@@ -50,7 +49,6 @@ def on_message_depth(_ws, message):
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})
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check_and_train_model()
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predict_market_direction()
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- show_message()
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def extract_features(order_book, trade):
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@@ -140,10 +138,11 @@ def predict_market_direction():
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feature_vector = np.array([list(features.values())])
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prediction = model.predict(feature_vector)
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logger.info("Predicted Market Direction: %s", "Up" if prediction[0] == 1 else "Down")
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+ show_message(prediction[0])
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# 将消息推送到外部，看看图长什么样
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-def show_message():
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+def show_message(market_direction):
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global order_book_snapshots, trade_data
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if len(order_book_snapshots) > 0 and len(trade_data) > 0:
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@@ -152,8 +151,8 @@ def show_message():
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latest_trade = trade_data[-1]
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# 提取asks和bids数据
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- asks = [[float(price), float(qty)] for price, qty in latest_order_book['asks']]
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- bids = [[float(price), float(qty)] for price, qty in latest_order_book['bids']]
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+ asks = [[float(price), 1 if market_direction == 1 else 0] for price, qty in latest_order_book['asks']]
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+ bids = [[float(price), 1 if market_direction == 0 else 0] for price, qty in latest_order_book['bids']]
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# 排序asks和bids数据
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asks_sorted = sorted(asks, key=lambda x: x[0])
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