0.9.59 update

czsc/utils/ta.py

@@ -6,6 +6,7 @@
 describe: 常用技术分析指标
 """
 import numpy as np
+import pandas as pd
 
 
 def SMA(close: np.array, timeperiod=5):
@@ -22,9 +23,9 @@ def SMA(close: np.array, timeperiod=5):
     res = []
     for i in range(len(close)):
         if i < timeperiod:
-            seq = close[0: i + 1]
+            seq = close[0 : i + 1]
         else:
-            seq = close[i - timeperiod + 1: i + 1]
+            seq = close[i - timeperiod + 1 : i + 1]
         res.append(seq.mean())
     return np.array(res, dtype=np.double).round(4)
 
@@ -85,11 +86,11 @@ def KDJ(close: np.array, high: np.array, low: np.array):
     lv = []
     for i in range(len(close)):
         if i < n:
-            h_ = high[0: i + 1]
-            l_ = low[0: i + 1]
+            h_ = high[0 : i + 1]
+            l_ = low[0 : i + 1]
         else:
-            h_ = high[i - n + 1: i + 1]
-            l_ = low[i - n + 1: i + 1]
+            h_ = high[i - n + 1 : i + 1]
+            l_ = low[i - n + 1 : i + 1]
         hv.append(max(h_))
         lv.append(min(l_))
 
@@ -143,3 +144,158 @@ def RSQ(close: [np.array, list]) -> float:
     rsq = 1 - ss_err / ss_tot
 
     return round(rsq, 4)
+
+
+def plus_di(high, low, close, timeperiod=14):
+    """
+    Calculate Plus Directional Indicator (PLUS_DI) manually.
+
+    Parameters:
+    high (pd.Series): High price series.
+    low (pd.Series): Low price series.
+    close (pd.Series): Closing price series.
+    timeperiod (int): Number of periods to consider for the calculation.
+
+    Returns:
+    pd.Series: Plus Directional Indicator values.
+    """
+    # Calculate the +DM (Directional Movement)
+    dm_plus = high - high.shift(1)
+    dm_plus[dm_plus < 0] = 0  # Only positive differences are considered
+
+    # Calculate the True Range (TR)
+    tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
+
+    # Smooth the +DM and TR with Wilder's smoothing method
+    smooth_dm_plus = dm_plus.rolling(window=timeperiod).sum()
+    smooth_tr = tr.rolling(window=timeperiod).sum()
+
+    # Avoid division by zero
+    smooth_tr[smooth_tr == 0] = np.nan
+
+    # Calculate the Directional Indicator
+    plus_di_ = 100 * (smooth_dm_plus / smooth_tr)
+
+    return plus_di_
+
+
+def minus_di(high, low, close, timeperiod=14):
+    """
+    Calculate Minus Directional Indicator (MINUS_DI) manually.
+
+    Parameters:
+    high (pd.Series): High price series.
+    low (pd.Series): Low price series.
+    close (pd.Series): Closing price series.
+    timeperiod (int): Number of periods to consider for the calculation.
+
+    Returns:
+    pd.Series: Minus Directional Indicator values.
+    """
+    # Calculate the -DM (Directional Movement)
+    dm_minus = (low.shift(1) - low).where((low.shift(1) - low) > (high - low.shift(1)), 0)
+
+    # Smooth the -DM with Wilder's smoothing method
+    smooth_dm_minus = dm_minus.rolling(window=timeperiod).sum()
+
+    # Calculate the True Range (TR)
+    tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
+
+    # Smooth the TR with Wilder's smoothing method
+    smooth_tr = tr.rolling(window=timeperiod).sum()
+
+    # Avoid division by zero
+    smooth_tr[smooth_tr == 0] = pd.NA
+
+    # Calculate the Directional Indicator
+    minus_di_ = 100 * (smooth_dm_minus / smooth_tr.fillna(method="ffill"))
+
+    return minus_di_
+
+
+def atr(high, low, close, timeperiod=14):
+    """
+    Calculate Average True Range (ATR).
+
+    Parameters:
+    high (pd.Series): High price series.
+    low (pd.Series): Low price series.
+    close (pd.Series): Closing price series.
+    timeperiod (int): Number of periods to consider for the calculation.
+
+    Returns:
+    pd.Series: Average True Range values.
+    """
+    # Calculate True Range (TR)
+    tr1 = high - low
+    tr2 = (high - close.shift()).abs()
+    tr3 = (close.shift() - low).abs()
+    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+    # Calculate ATR
+    atr_ = tr.rolling(window=timeperiod).mean()
+
+    return atr_
+
+
+def MFI(high, low, close, volume, timeperiod=14):
+    """
+    Calculate Money Flow Index (MFI).
+
+    Parameters:
+    high (np.array): Array of high prices.
+    low (np.array): Array of low prices.
+    close (np.array): Array of closing prices.
+    volume (np.array): Array of trading volumes.
+    timeperiod (int): Number of periods to consider for the calculation.
+
+    Returns:
+    np.array: Array of Money Flow Index values.
+    """
+    # Calculate Typical Price
+    typical_price = (high + low + close) / 3
+
+    # Calculate Raw Money Flow
+    raw_money_flow = typical_price * volume
+
+    # Calculate Positive and Negative Money Flow
+    positive_money_flow = np.where(typical_price > typical_price.shift(1), raw_money_flow, 0)
+    negative_money_flow = np.where(typical_price < typical_price.shift(1), raw_money_flow, 0)
+
+    # Calculate Money Ratio
+    money_ratio = (
+        positive_money_flow.rolling(window=timeperiod).sum() / negative_money_flow.rolling(window=timeperiod).sum()
+    )
+
+    # Calculate Money Flow Index
+    mfi = 100 - (100 / (1 + money_ratio))
+
+    return mfi
+
+
+def CCI(high, low, close, timeperiod=14):
+    """
+    Calculate Commodity Channel Index (CCI).
+
+    Parameters:
+    high (np.array): Array of high prices.
+    low (np.array): Array of low prices.
+    close (np.array): Array of closing prices.
+    timeperiod (int): Number of periods to consider for the calculation.
+
+    Returns:
+    np.array: Array of Commodity Channel Index values.
+    """
+    # Typical Price
+    typical_price = (high + low + close) / 3
+
+    # Mean Deviation
+    mean_typical_price = np.mean(typical_price, axis=0)
+    mean_deviation = np.mean(np.abs(typical_price - mean_typical_price), axis=0)
+
+    # Constant
+    constant = 1 / (0.015 * timeperiod)
+
+    # CCI Calculation
+    cci = (typical_price - mean_typical_price) / (constant * mean_deviation)
+    return cci

requirements.txt

@@ -27,4 +27,5 @@ optuna
 cryptography
 pytz
 flask
-scipy
+scipy
+requests_toolbelt