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@@ -12,9 +12,9 @@ from logger_config import logger
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getcontext().prec = 28
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# 假设我们有一个数据流，订单簿和成交数据
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-order_book_snapshots = deque(maxlen=60) # 存储过去60个订单簿快照
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-spread_delta_snapshots = deque(maxlen=60) # 存储过去60个价差数据(最小变动价格的倍数)
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-trade_snapshots = deque(maxlen=600) # 存储过去600个成交数据
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+order_book_snapshots = deque(maxlen=100) # 存储订单簿快照
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+spread_delta_snapshots = deque(maxlen=100) # 存储价差数据(最小变动价格的倍数)
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+trade_snapshots = deque(maxlen=1000) # 存储成交数据
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stop_event = threading.Event()
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@@ -29,6 +29,10 @@ bounds = [(10, 1000.0), # A 的范围
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# 假设S0是初始的参考价格
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S0 = -1
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+# 记录最大挂单距离，并实时刷新
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+max_delta_sum = 0
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+
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+
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def get_tick_size_from_prices(ask_price, bid_price):
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# 获取价格的小数位数
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ask_decimal_places = len(str(ask_price).split('.')[1])
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@@ -172,6 +176,7 @@ def calculate_sigma_squared(prices, timestamps):
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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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+ global max_delta_sum
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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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@@ -225,4 +230,6 @@ def process_depth_data():
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gamma = 1.0
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T_minus_t = 1.0
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delta_sum = calculate_delta_sum(gamma, sigma_squared, T_minus_t, k_initial)
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- logger.info(f"δ^a + δ^b: {delta_sum}, k: {k_initial}, dm: {delta_max}, λ(δ): {lambda_hat}, ∫ φ(k, ξ) dξ: {integral_phi_value}")
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+ if delta_sum > max_delta_sum and len(trade_snapshots) > 70:
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+ logger.info(f"δ^a + δ^b: {delta_sum}, k: {k_initial}, dm: {delta_max}, λ(δ): {lambda_hat}, ∫ φ(k, ξ) dξ: {integral_phi_value}")
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+ max_delta_sum = delta_sum
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