VeighNa CTA Indicator Functions and Their Inputs
Article vn.py
Summary
This reference catalogs calculation functions available in the VeighNa Elite Trader CTA module. It groups common tools by their required inputs and outputs, covering moving averages, momentum and rate-of-change measures, volatility, trend strength and direction, volume-based indicators, oscillators, and price channels. It also lists crossover and monotonic-sequence checks, plus a helper for resampling bar data into a chosen interval.
The entries specify expected array inputs, period parameters, and returned arrays; some functions accept alternate moving-average types or multiple time periods. A short example shows obtaining a bar-data frame and resampling it to five-minute bars. This makes the page useful as an API-oriented inventory for strategy developers choosing calculations to call. It does not explain the formulas, signal interpretation, data warm-up requirements, or performance implications, and it provides no trading rules or test results. Users still need to consult implementation details and validate how outputs behave on their own data.
Key ideas
- The CTA module includes moving-average, momentum, volatility, trend, volume, oscillator, and channel calculations.
- Function entries specify input arrays, configurable periods, and returned arrays.
- Crossover and monotonicity helpers support checks on recent data sequences.
- A resampling helper converts bar data to another interval, illustrated with five-minute bars.
- The reference lists interfaces but does not explain formulas or evaluate trading performance.
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# 指标计算函数 # 指标计算函数 VeighNa Elite Trader的CTA策略模块内置了以下计算函数供策略调用： **sma** ：简单移动平均 * 入参 * close: np.ndarray * time_period: int=30 * 出参 * sma_array: np.ndarray **ema** ：指数滑动平均 * 入参 * close: np.ndarray * time_period: int=30 * 出参 * ema_array: np.ndarray **kama** ：适应性移动平均 * 入参 * close: np.ndarray * time_period: int=30 * 出参 * atr_array: np.ndarray **wma** ：加权移动平均 * 入参 * close: np.ndarray * time_period: int=30 * 出参 * wma_array: np.ndarray **apo**：绝对价格振荡器 * 入参 * close: np.ndarray * fast_period: int=12 * slow_period: int=26 * matype: int=0 * 出参 * apo_array: np.ndarray 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 **cmo**：钱德动量摆动指标 * 入参 * close: np.ndarray * time_period: int=14 * 出参 * cmo_array: np.ndarray **mom**：上升动向值 * 入参 * close: np.ndarray * time_period: int=10 * 出参 * mom_array: np.ndarray **ppo**：价格震荡百分比指数 * 入参 * close: np.ndarray * fast_period: int=12 * slow_period: int=26 * matype: int=0 * 出参 * ppo_array: np.ndarray **roc**：变动率指标 * 入参 * close: np.ndarray * time_period: int=10 * 出参 * roc_array: np.ndarray **rocr**：变动率比率 * 入参 * close: np.ndarray * time_period: int=10 * 出参 * rocr_array: np.ndarray **rocp**：变动率百分比 * 入参 * close: np.ndarray * time_period: int=10 * 出参 * rocp_array: np.ndarray **trix**：三次平滑EMA的一天变化率 * 入参 * close: np.ndarray * time_period: int=30 * 出参 * trix_array: np.ndarray **stddev**：标准偏差 * 入参 * close: np.ndarray * time_period: int=5 * nbdev: float=1 * 出参 * stddev_array: np.ndarray **std**：标准偏差 * 入参 * close: np.ndarray * time_period: int=5 * nbdev: float=1 * 出参 * std_array: np.ndarray **obv**：能量潮 * 入参 * close: np.ndarray * volume: np.ndarray * 出参 * obv_array: np.ndarray **cci**：顺势指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * cci_array: np.ndarray **atr**：真实波动幅度均值 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * atr_array: np.ndarray **natr**：归一化波动幅度均值 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * natr_array: np.ndarray **rsi**：相对强弱指数 * 入参 * close: np.ndarray * time_period: int=14 * 出参 * rsi_array: np.ndarray **macd**：平均异同移动平均线 * 入参 * close: np.ndarray * fast_period: int=12 * slow_period: int=26 * signal_period: int=9 * 出参 * macd_array: np.ndarray * macdsignal_array: np.ndarray * macdhist_array: np.ndarray **adx**：平均趋向指数 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * adx_array: np.ndarray **adxr**：平均趋向指数的趋向指数 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * adxr_array: np.ndarray **minus_di**：负趋向指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * minusdi_array: np.ndarray **plus_di**：正趋向指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * plusdi_array: np.ndarray **willr**：威廉指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=14 * 出参 * willr_array: np.ndarray **ultosc**：终极波动指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * time_period: int=7 * time_period2: int=14 * time_period3: int=28 * 出参 * ultosc_array: np.ndarray **trange**：真实波幅 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * 出参 * trange_array: np.ndarray **aroon**：阿隆指标 * 入参 * high: np.ndarray * low: np.ndarray * time_period: int=14 * 出参 * aroonup_array: np.ndarray * aroondown_array: np.ndarray **aroonosc**：阿隆震荡 * 入参 * high: np.ndarray * low: np.ndarray * time_period: int=14 * 出参 * aroonosc_array: np.ndarray **minus_dm**：负趋向变动值 * 入参 * high: np.ndarray * low: np.ndarray * time_period: int=14 * 出参 * minusdm_array: np.ndarray **plus_dm**：正趋向变动值 * 入参 * high: np.ndarray * low: np.ndarray * time_period: int=14 * 出参 * plusdm_array: np.ndarray **mfi**：资金流量指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * volume: np.ndarray * time_period: int=14 * 出参 * mfi_array: np.ndarray **ad**：平衡交易量指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * volume: np.ndarray * 出参 * ad_array: np.ndarray **adosc**：震荡指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * volume: np.ndarray * fast_period: int=3 * slow_period: int=10 * 出参 * adosc_array: np.ndarray **bop**：均势指标 * 入参 * open: np.ndarray * high: np.ndarray * low: np.ndarray * close: np.ndarray * 出参 * bop_array: np.ndarray **stoch**：随机指标 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * fastk_period: int=5 * slowk_period: int=3 * slowk_matype: int=0 * slowd_period: int=3 * slowd_matype: int=0 * 出参 * slowk_array: np.ndarray * slowd_array: np.ndarray **boll**：布林通道 * 入参 * data: np.ndarray * window: int * dev: float * 出参 * bollup_array: np.ndarray * bolldown_array: np.ndarray **keltner**：肯特纳通道 * 入参 * high: np.ndarray * low: np.ndarray * close: np.ndarray * window: int * dev: float * 出参 * kkup_array: np.ndarray * kkdown_array: np.ndarray **donchian**：唐奇安通道 * 入参 * high: np.ndarray * low: np.ndarray * window: int * 出参 * donchianup_array: np.ndarray * donchiandown_array: np.ndarray **cross_over**：上穿 * 入参 * data: np.ndarray * level: float 若data上一个值小于等于level以及data最新值大于level，则返回True。 * 出参 * cross_over: bool **cross_below**：下穿 * 入参 * data: np.ndarray * level: float * 出参 * cross_below: bool 若data上一个值大于等于level以及data最新值小于level，则返回True。 **check_increasing**：检查序列单调上升 * 入参 * data: np.ndarray * 出参 * increasing: bool **check_decreasing**：检查序列单调下降 * 入参 * data: np.ndarray * 出参 * decreasing: bool **resample_data**：对K线数据重新取样 * 入参 * df: pd.DataFrame * rule: str * 出参 * resampled_df: pd.DataFrame * 示例 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： ```python3 # 判断实盘trading状态，只有策略启动之后才进行输出 df: pd.DataFrame = hm.to_dataframe() resampled_df: pd.DataFrame = resample_data(df, "5min") ```
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