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@@ -20,8 +20,8 @@ trade_snapshots = deque(maxlen=6000) # 存储过去6000个成交数
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stop_event = threading.Event()
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# 初始参数
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-k_initial = 10
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-A_initial = 100
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+k_initial = 6
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+A_initial = 140
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# 定义参数范围
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bounds = [(10, 1000.0), # A 的范围
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@@ -149,8 +149,8 @@ def process_depth_data():
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global order_book_snapshots, trade_snapshots, spread_delta_snapshots
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global k_initial, A_initial, S0
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- # 数据预热，至少10条深度数据以及100条成交数据才能用于计算
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- if len(order_book_snapshots) < 10 or len(trade_snapshots) < 100:
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+ # 数据预热，至少3条深度数据以及10条成交数据才能用于计算
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+ if len(order_book_snapshots) < 3 or len(trade_snapshots) < 10:
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return
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S_values = [((snapshot['bids'][0][0] + snapshot['asks'][0][0]) / 2) for snapshot in order_book_snapshots]
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@@ -193,8 +193,8 @@ def process_depth_data():
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if result.success:
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A_initial, k_initial = result.x
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- logger.info(f"Optimal A: {A_initial}, Optimal k: {k_initial}")
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- else:
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- logger.error("Optimization failed")
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+ logger.info(f"Optimal k: {k_initial}, delta_max: {delta_max}")
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+ # else:
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+ # logger.error("Optimization failed")
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# logger.info("log(λ(δ)): {}, log(∫ φ(k, ξ) dξ): {}".format(log_lambda_hat_value, log_integral_phi_value))
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