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@@ -80,6 +80,11 @@ historical_data_points = deque(maxlen=MAX_HISTORY_POINTS_PLOTLY)
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TARGET_ASSET_SYMBOL = CEX_TARGET_PAIR_USDT.split('_')[0] # e.g., RATO
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BASE_CURRENCY_SYMBOL = CEX_TARGET_PAIR_USDT.split('_')[1] # e.g., USDT (assumed to be consistent with IN_TOKEN_ADDRESS)
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+# 价差与平均价差
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+GAMMA = decimal.Decimal(0.9999)
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+GAMMA_1 = decimal.Decimal(0.99)
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+spread_ema = decimal.Decimal(0)
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+count = decimal.Decimal(0)
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# --- 链上价格获取函数 (Okx) ---
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# 返回: price_base_per_target (例如 USDT per RATO)
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def get_chain_price_vs_target_currency(chain_id, in_token_addr, out_token_addr, amount_in_base_human, in_token_decimals, slippage, user_wallet_addr, user_exchange_wallet_addr):
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@@ -298,6 +303,19 @@ def update_data_for_plotly_and_table():
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dex_price = erc20_data.get("price_base_per_target") # USDT/TARGET
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erc20_err = erc20_data.get("error")
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+ # 加入价差，考虑spread重新计算dex_price
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+ global spread_ema, count
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+ spread = dex_price - cex_price
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+ if count < 120:
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+ count = count + 1
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+ if count == 1:
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+ spread_ema = spread
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+ else:
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+ spread_ema = spread_ema * GAMMA_1 + spread * (decimal.Decimal(1) - GAMMA_1)
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+ else:
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+ spread_ema = spread_ema * GAMMA + spread * (decimal.Decimal(1) - GAMMA)
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+ dex_price = dex_price - spread_ema
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+
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# 4. 计算百分比差异
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# diff = (CEX卖价 - 链上买价) / 链上买价
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diff_erc20_vs_cex_pct = calculate_percentage_diff(
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