wehub-resource-sync
476 lines
7.3 KiB
Markdown
476 lines
7.3 KiB
Markdown
# 指标计算函数
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VeighNa Elite Trader的CTA策略模块内置了以下计算函数供策略调用：
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**sma** ：简单移动平均
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* 入参
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* close: np.ndarray
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* time_period: int=30
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* 出参
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* sma_array: np.ndarray
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**ema** ：指数滑动平均
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* 入参
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* close: np.ndarray
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* time_period: int=30
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* 出参
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* ema_array: np.ndarray
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**kama** ：适应性移动平均
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* 入参
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* close: np.ndarray
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* time_period: int=30
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* 出参
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* atr_array: np.ndarray
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**wma** ：加权移动平均
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* 入参
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* close: np.ndarray
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* time_period: int=30
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* 出参
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* wma_array: np.ndarray
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**apo**：绝对价格振荡器
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* 入参
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* close: np.ndarray
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* fast_period: int=12
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* slow_period: int=26
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* matype: int=0
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* 出参
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* apo_array: np.ndarray
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请注意，matype分别对应：0=SMA, 1=EMA, 2=WMA, 3=DEMA, 4=TEMA, 5=TRIMA, 6=KAMA, 7=MAMA, 8=T3
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**cmo**：钱德动量摆动指标
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* 入参
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* cmo_array: np.ndarray
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**mom**：上升动向值
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* 入参
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* close: np.ndarray
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* time_period: int=10
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* 出参
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* mom_array: np.ndarray
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**ppo**：价格震荡百分比指数
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* 入参
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* close: np.ndarray
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* fast_period: int=12
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* slow_period: int=26
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* matype: int=0
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* 出参
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* ppo_array: np.ndarray
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**roc**：变动率指标
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* 入参
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* close: np.ndarray
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* time_period: int=10
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* 出参
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* roc_array: np.ndarray
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**rocr**：变动率比率
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* 入参
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* close: np.ndarray
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* time_period: int=10
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* 出参
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* rocr_array: np.ndarray
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**rocp**：变动率百分比
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* 入参
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* close: np.ndarray
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* time_period: int=10
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* 出参
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* rocp_array: np.ndarray
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**trix**：三次平滑EMA的一天变化率
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* 入参
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* close: np.ndarray
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* time_period: int=30
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* 出参
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* trix_array: np.ndarray
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**stddev**：标准偏差
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* 入参
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* close: np.ndarray
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* time_period: int=5
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* nbdev: float=1
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* 出参
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* stddev_array: np.ndarray
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**std**：标准偏差
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* 入参
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* close: np.ndarray
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* time_period: int=5
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* nbdev: float=1
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* 出参
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* std_array: np.ndarray
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**obv**：能量潮
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* 入参
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* close: np.ndarray
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* volume: np.ndarray
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* 出参
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* obv_array: np.ndarray
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**cci**：顺势指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* cci_array: np.ndarray
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**atr**：真实波动幅度均值
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* atr_array: np.ndarray
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**natr**：归一化波动幅度均值
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* natr_array: np.ndarray
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**rsi**：相对强弱指数
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* 入参
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* rsi_array: np.ndarray
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**macd**：平均异同移动平均线
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* 入参
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* close: np.ndarray
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* fast_period: int=12
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* slow_period: int=26
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* signal_period: int=9
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* 出参
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* macd_array: np.ndarray
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* macdsignal_array: np.ndarray
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* macdhist_array: np.ndarray
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**adx**：平均趋向指数
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* adx_array: np.ndarray
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**adxr**：平均趋向指数的趋向指数
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* adxr_array: np.ndarray
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**minus_di**：负趋向指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* minusdi_array: np.ndarray
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**plus_di**：正趋向指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* plusdi_array: np.ndarray
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**willr**：威廉指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=14
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* 出参
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* willr_array: np.ndarray
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**ultosc**：终极波动指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* time_period: int=7
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* time_period2: int=14
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* time_period3: int=28
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* 出参
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* ultosc_array: np.ndarray
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**trange**：真实波幅
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* 出参
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* trange_array: np.ndarray
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**aroon**：阿隆指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* time_period: int=14
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* 出参
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* aroonup_array: np.ndarray
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* aroondown_array: np.ndarray
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**aroonosc**：阿隆震荡
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* time_period: int=14
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* 出参
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* aroonosc_array: np.ndarray
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**minus_dm**：负趋向变动值
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* time_period: int=14
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* 出参
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* minusdm_array: np.ndarray
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**plus_dm**：正趋向变动值
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* time_period: int=14
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* 出参
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* plusdm_array: np.ndarray
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**mfi**：资金流量指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* volume: np.ndarray
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* time_period: int=14
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* 出参
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* mfi_array: np.ndarray
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**ad**：平衡交易量指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* volume: np.ndarray
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* 出参
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* ad_array: np.ndarray
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**adosc**：震荡指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* volume: np.ndarray
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* fast_period: int=3
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* slow_period: int=10
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* 出参
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* adosc_array: np.ndarray
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**bop**：均势指标
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* 入参
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* open: np.ndarray
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* 出参
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* bop_array: np.ndarray
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**stoch**：随机指标
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* fastk_period: int=5
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* slowk_period: int=3
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* slowk_matype: int=0
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* slowd_period: int=3
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* slowd_matype: int=0
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* 出参
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* slowk_array: np.ndarray
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* slowd_array: np.ndarray
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**boll**：布林通道
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* 入参
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* data: np.ndarray
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* window: int
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* dev: float
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* 出参
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* bollup_array: np.ndarray
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* bolldown_array: np.ndarray
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**keltner**：肯特纳通道
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* close: np.ndarray
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* window: int
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* dev: float
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* 出参
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* kkup_array: np.ndarray
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* kkdown_array: np.ndarray
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**donchian**：唐奇安通道
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* 入参
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* high: np.ndarray
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* low: np.ndarray
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* window: int
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* 出参
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* donchianup_array: np.ndarray
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* donchiandown_array: np.ndarray
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**cross_over**：上穿
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* 入参
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* data: np.ndarray
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* level: float
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若data上一个值小于等于level以及data最新值大于level，则返回True。
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* 出参
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* cross_over: bool
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**cross_below**：下穿
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* 入参
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* data: np.ndarray
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* level: float
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* 出参
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* cross_below: bool
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若data上一个值大于等于level以及data最新值小于level，则返回True。
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**check_increasing**：检查序列单调上升
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* 入参
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* data: np.ndarray
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* 出参
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* increasing: bool
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**check_decreasing**：检查序列单调下降
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* 入参
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* data: np.ndarray
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* 出参
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* decreasing: bool
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**resample_data**：对K线数据重新取样
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* 入参
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* df: pd.DataFrame
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* rule: str
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* 出参
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* resampled_df: pd.DataFrame
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* 示例
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若想要测试resample_data函数的效果，可以在策略的on_history函数收到hm推送的时候先获取K线DataFrame，再调用resample_data函数对K线数据重新取样，如下所示：
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```python3
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# 判断实盘trading状态，只有策略启动之后才进行输出
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df: pd.DataFrame = hm.to_dataframe()
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resampled_df: pd.DataFrame = resample_data(df, "5min")
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```
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