基本技术指标 Python 实现(1)

* Middle Band = 20-day simple moving average (SMA)
  * Upper Band = 20-day SMA + (20-day standard deviation of price x 2) 
  * Lower Band = 20-day SMA - (20-day standard deviation of price x 2)

名称：布林带计算
描述：根据给定的DataFrame计算布林带指标
代码：
def bollinger_bands(df):
    df['Middle Band'] = df['close'].rolling(window=20).mean()
    df['Upper Band'] = df['Middle Band'] + (df['close'].rolling(window=20).std() * 2)
    df['Lower Band'] = df['Middle Band'] - (df['close'].rolling(window=20).std() * 2)
    return df

Chandelier Exit (long) = 22-day High - ATR(22) x 3 
Chandelier Exit (short) = 22-day Low + ATR(22) x 3

# 计算吊灯退出指标
def chandelier_exit(df):
    # 计算 ATR
    df['TR'] = df['high'] - df['low']
    df['TR1'] = abs(df['high'] - df['close'].shift())
    df['TR2'] = abs(df['low'] - df['close'].shift())
    df['TR'] = df[['TR', 'TR1', 'TR2']].max(axis=1)
    df['ATR'] = df['TR'].rolling(window=22).mean()
    
    # 计算吊灯退出（长仓和短仓）
    df['Chandelier_Exit_Long'] = df['high'].rolling(window=22).max() - df['ATR'] * 3
    df['Chandelier_Exit_Short'] = df['low'].rolling(window=22).min() + df['ATR'] * 3
    
    return df[['Chandelier_Exit_Long', 'Chandelier_Exit_Short']]
# 使用示例
result = chandelier_exit(df)
print(result)

Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2))
The default setting is 9 periods and can be adjusted. On a daily chart, this line is the midpoint of the 9-day high-low range, 
which is almost two weeks.  

Kijun-sen (Base Line): (26-period high + 26-period low)/2))
The default setting is 26 periods and can be adjusted. On a daily chart, this line is the midpoint of the 26-day high-low range, which is almost one month).  

Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
This is the midpoint between the Conversion Line and the Base Line. The Leading Span A forms one of the two Cloud boundaries. It is referred to as "Leading" because it is plotted 26 periods in the future and forms the faster Cloud boundary. 

Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
On the daily chart, this line is the midpoint of the 52-day high-low range, which is a little less than 3 months. The default calculation setting is 52 periods, but can be adjusted. This value is plotted 26 periods in the future and forms the slower Cloud boundary.

Chikou Span (Lagging Span): Close plotted 26 days in the past
The default setting is 26 periods, but can be adjusted. 

名称：一目均衡云计算
描述：根据给定的开盘价、最高价、最低价和收盘价计算一目均衡云中的各个指标
代码：
def calculate_ichimoku_cloud(df):
    # Tenkan-sen (Conversion Line)
    nine_period_high = df['high'].rolling(window=9).max()
    nine_period_low = df['low'].rolling(window=9).min()
    df['tenkan_sen'] = (nine_period_high + nine_period_low) / 2
    
    # Kijun-sen (Base Line)
    twenty_six_period_high = df['high'].rolling(window=26).max()
    twenty_six_period_low = df['low'].rolling(window=26).min()
    df['kijun_sen'] = (twenty_six_period_high + twenty_six_period_low) / 2
    
    # Senkou Span A (Leading Span A)
    df['senkou_span_a'] = ((df['tenkan_sen'] + df['kijun_sen']) / 2).shift(26)
    
    # Senkou Span B (Leading Span B)
    fifty_two_period_high = df['high'].rolling(window=52).max()
    fifty_two_period_low = df['low'].rolling(window=52).min()
    df['senkou_span_b'] = ((fifty_two_period_high + fifty_two_period_low) / 2).shift(26)
    
    # Chikou Span (Lagging Span)
    df['chikou_span'] = df['close'].shift(-26)
    
    return df

## 计算
名称：{KAMA}
描述：计算考夫曼自适应移动平均线（KAMA）
--- 代码：
def KAMA(df):
    er_window = 10
    fast_period = 2
    slow_period = 30
    
    df['change'] = df['close'] - df['close'].shift(1)
    df['volatility'] = abs(df['close'] - df['close'].shift(er_window))
    
    df['er'] = df['change'] / df['volatility']
    
    sc_fast = 2 / (fast_period + 1)
    sc_slow = 2 / (slow_period + 1)
    
    df['sc'] = (df['er'] * (sc_fast - sc_slow) + sc_slow) ** 2
    
    df['kama'] = 0.0
    
    for i in range(er_window, len(df)):
        if df['kama'][i-1] != 0:
            df['kama'][i] = df['kama'][i-1] + df['sc'][i] * (df['close'][i] - df['kama'][i-1])
        else:
            df['kama'][i] = df['close'][i]
    
    return df['kama']

Middle Line: 20-day exponential moving average 
Upper Channel Line: 20-day EMA + (2 x ATR(10))
Lower Channel Line: 20-day EMA - (2 x ATR(10))

名称：Keltner通道
描述：根据给定的参数计算Keltner通道的三条线
def keltner_channel(df, ema_length=20, atr_length=10, multiplier=2):
    df['EMA'] = df['close'].ewm(span=ema_length, adjust=False).mean()
    df['ATR'] = df['high'].rolling(window=atr_length).max() - df['low'].rolling(window=atr_length).min()
    
    df['Upper_Channel'] = df['EMA'] + (multiplier * df['ATR'])
    df['Lower_Channel'] = df['EMA'] - (multiplier * df['ATR'])
    
    return df[['EMA', 'Upper_Channel', 'Lower_Channel']]

Daily Closing Prices: 11,12,13,14,15,16,17
First day of 5-day SMA: (11 + 12 + 13 + 14 + 15) / 5 = 13
Second day of 5-day SMA: (12 + 13 + 14 + 15 + 16) / 5 = 14
Third day of 5-day SMA: (13 + 14 + 15 + 16 + 17) / 5 = 15

名称：simple_moving_average
描述：计算简单移动平均线
--- 
代码：
def simple_moving_average(df, window):
    df['SMA'] = df['close'].rolling(window=window).mean()
    return df

Upper Envelope: 20-day SMA + (20-day SMA x .025)
Lower Envelope: 20-day SMA - (20-day SMA x .025)

名称：移动平均包络
描述：根据给定的DataFrame计算移动平均包络
代码：
def moving_average_envelope(df, window=20, percentage=0.025):
    df['SMA'] = df['close'].rolling(window=window).mean()
    df['Upper Envelope'] = df['SMA'] + (df['SMA'] * percentage)
    df['Lower Envelope'] = df['SMA'] - (df['SMA'] * percentage)
    
    return df[['SMA', 'Upper Envelope', 'Lower Envelope']]

Rising SAR
----------
Prior SAR: The SAR value for the previous period. 
Extreme Point (EP): The highest high of the current uptrend. 
Acceleration Factor (AF): Starting at .02, AF increases by .02 each time the extreme point makes a new high. AF can reach a maximum of .20, no matter how long the uptrend extends. 
Current SAR = Prior SAR + Prior AF(Prior EP - Prior SAR)
13-Apr-10 SAR = 48.28 = 48.13 + .14(49.20 - 48.13)
The Acceleration Factor is multiplied by the difference between the Extreme Point and the prior period's SAR. This is then added to the prior period's SAR. Note however that SAR can never be above the prior two periods' lows. Should SAR be above one of those lows, use the lowest of the two for SAR. 

Falling SAR
-----------
Prior SAR: The SAR value for the previous period. 
Extreme Point (EP): The lowest low of the current downtrend. 
Acceleration Factor (AF): Starting at .02, AF increases by .02 each time the extreme point makes a new low. AF can reach a maximum of .20, no matter how long the downtrend extends. 
Current SAR = Prior SAR - Prior AF(Prior SAR - Prior EP)
9-Feb-10 SAR = 43.56 = 43.84 - .16(43.84 - 42.07)
The Acceleration Factor is multiplied by the difference between the Prior period's SAR and the Extreme Point. This is then subtracted from the prior period's SAR. Note however that SAR can never be below the prior two periods' highs. Should SAR be below one of those highs, use the highest of the two for SAR. 

名称：SAR计算
描述：根据给定的开盘价、最高价、最低价和收盘价计算抛物线 SAR 指标
import pandas as pd
def calculate_SAR(df):
    df['SAR'] = 0
    df['AF'] = 0
    df['EP'] = 0
    
    for i in range(2, len(df)):
        if df['high'][i-1] > df['high'][i-2] and df['low'][i-1] > df['low'][i-2]:
            df.at[i, 'AF'] = min(df['AF'][i-1] + 0.02, 0.2)
        elif df['high'][i-1] < df['high'][i-2] and df['low'][i-1] < df['low'][i-2]:
            df.at[i, 'AF'] = max(df['AF'][i-1] - 0.02, 0.02)
        else:
            df.at[i, 'AF'] = df['AF'][i-1]
        
        if df['AF'][i] == 0.2:
            df.at[i, 'EP'] = df['high'][i]
        else:
            df.at[i, 'EP'] = df['EP'][i-1]
        
        if df['AF'][i] > 0:
            if df['AF'][i-1] > 0:
                if df['high'][i] > df['EP'][i-1]:
                    df.at[i, 'SAR'] = df['SAR'][i-1] + df['AF'][i-1] * (df['EP'][i-1] - df['SAR'][i-1])
                else:
                    df.at[i, 'SAR'] = df['EP'][i-1]
            else:
                df.at[i, 'SAR'] = df['EP'][i-1]
        else:
            if df['AF'][i-1] < 0:
                if df['low'][i] < df['EP'][i-1]:
                    df.at[i, 'SAR'] = df['SAR'][i-1] - df['AF'][i-1] * (df['SAR'][i-1] - df['EP'][i-1])
                else:
                    df.at[i, 'SAR'] = df['EP'][i-1]
            else:
                df.at[i, 'SAR'] = df['EP'][i-1]
    
    return df
# 使用示例
df = pd.DataFrame({
    'open': [50, 48, 47, 45, 46],
    'high': [52, 50, 49, 48, 47],
    'low': [48, 46, 45, 43, 44],
    'close': [49, 47, 46, 44, 45]
})
df = calculate_SAR(df)
print(df)

名称：{title}
描述：计算股票的收益率
---
代码：
def calculate_returns(df):
    df['returns'] = df['close'].pct_change()
    return df

Upper Channel Line: 20-day high
Lower Channel Line: 20-day low
Centerline: (20-day high + 20-day low)/2 

# 计算价格通道指标
def calculate_price_channel(df):
    df['Upper Channel Line'] = df['high'].rolling(window=20).max()
    df['Lower Channel Line'] = df['low'].rolling(window=20).min()
    df['Centerline'] = (df['Upper Channel Line'] + df['Lower Channel Line']) / 2
    
    return df

There are four steps involved in the calculation. 
This example is based on closing prices and the default parameter setting (12). 
  1\. Find the high-low range for closing prices for the entire period.  
  2\. Divide this range by 12 to create 12 equal price zones.
  3\. Total the amount of volume traded within each price zone.  
  4\. Divide the volume into up volume and down volume (optional). 

名称：成交量价格计算
描述：根据给定的收盘价和成交量数据，计算成交量价格
代码：
def calculate_volume_price(df):
    # Step 1: Find the high-low range for closing prices for the entire period
    price_range = df['close'].max() - df['close'].min()
    
    # Step 2: Divide the range by 12 to create 12 equal price zones
    price_zone = price_range / 12
    
    # Step 3: Total the amount of volume traded within each price zone
    df['price_zone'] = ((df['close'] - df['close'].min()) // price_zone).astype(int)
    volume_price = df.groupby('price_zone')['volume'].sum()
    
    # Step 4: Divide the volume into up volume and down volume
    df['volume_direction'] = df['close'].diff().apply(lambda x: 1 if x > 0 else -1)
    up_volume = df[df['volume_direction'] == 1].groupby('price_zone')['volume'].sum()
    down_volume = df[df['volume_direction'] == -1].groupby('price_zone')['volume'].sum()
    
    return volume_price, up_volume, down_volume

Cumulative(Volume x Typical Price)/Cumulative(Volume)

名称：VWAP 
描述：计算成交量加权平均价格
def vwap(df):
    typical_price = (df['high'] + df['low'] + df['close']) / 3
    cumulative_price_volume = (typical_price * df['volume']).cumsum()
    cumulative_volume = df['volume'].cumsum()
    vwap = cumulative_price_volume / cumulative_volume
    return vwap

名称：ZigZag
描述：根据给定的DataFrame计算ZigZag指标
import pandas as pd
def ZigZag(df):
    df['high_low_range'] = df['high'] - df['low']
    df['high_close_range'] = abs(df['high'] - df['close'])
    df['low_close_range'] = abs(df['low'] - df['close'])
    
    df['high_low_range_pct'] = df['high_low_range'] / df['close'] * 100
    df['high_close_range_pct'] = df['high_close_range'] / df['close'] * 100
    df['low_close_range_pct'] = df['low_close_range'] / df['close'] * 100
    
    df['zigzag'] = 0
    threshold = 5  # 设置ZigZag功能的灵敏度
    
    for i in range(1, len(df)):
        if df.loc[i, 'high_low_range_pct'] >= threshold:
            df.loc[i, 'zigzag'] = df.loc[i, 'close']
        elif df.loc[i, 'high_close_range_pct'] >= threshold:
            df.loc[i, 'zigzag'] = df.loc[i, 'close']
        elif df.loc[i, 'low_close_range_pct'] >= threshold:
            df.loc[i, 'zigzag'] = df.loc[i, 'close']
        else:
            df.loc[i, 'zigzag'] = df.loc[i-1, 'zigzag']
    
    return df
# 使用示例
# df = pd.read_csv('your_data.csv')
# df = ZigZag(df)