41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
# -*- coding: utf-8 -*-
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"""合成K线数据生成器：供测试使用（无网络依赖）"""
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import numpy as np
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import pandas as pd
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def gen_kline(n=120, base=10.0, trend=0.001, vol_pct=0.02, seed=42):
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"""
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生成合成日K线。
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trend: 每日平均漂移（如 0.001 = +0.1%/天）
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vol_pct: 每日随机波动幅度
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返回 DataFrame: [bar_time, open, high, low, close, volume, amount]
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"""
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rng = np.random.RandomState(seed)
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dates = pd.date_range('2025-01-01', periods=n, freq='B')
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close = np.zeros(n)
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close[0] = base
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for i in range(1, n):
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close[i] = close[i-1] * (1 + trend + rng.randn() * vol_pct)
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open_ = close * (1 + rng.randn(n) * vol_pct * 0.3)
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high = np.maximum(open_, close) * (1 + abs(rng.randn(n)) * vol_pct * 0.3)
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low = np.minimum(open_, close) * (1 - abs(rng.randn(n)) * vol_pct * 0.3)
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volume = np.abs(rng.randn(n)) * 1e6 + 5e5
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amount = volume * close
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return pd.DataFrame({
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'bar_time': dates.strftime('%Y-%m-%d'),
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'open': open_, 'high': high, 'low': low,
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'close': close, 'volume': volume,
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'amount': amount,
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})
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def gen_trending_up(n=80, base=10.0):
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"""持续上涨趋势K线"""
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return gen_kline(n=n, base=base, trend=0.008, vol_pct=0.01, seed=7)
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def gen_trending_down(n=80, base=50.0):
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"""持续下跌趋势K线"""
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return gen_kline(n=n, base=base, trend=-0.008, vol_pct=0.01, seed=7)
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