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第一次尝试将 ThinkScript 转换为 Python，我认为我的逻辑是正确的，但我遗漏了一些东西，因为指标的两个图不匹配。

尝试将 VPNIndicator 的 ThinkScript 转换为 Python 实现。寻找精通两种语言的人在这里做出贡献。

首先，ThinkorSwim 中的指标图如下所示（底部）：

ThinkorSwimPlot

所以我正在尝试使用 matplotlib Finance 复制该图，但首先我需要从 ThinkScript 转换为 Python，我在这里尝试过：

import mplfinance as mpf
import pandas as pd 
import numpy as np
import talib

def VPN_Indicator(df, params):

    # def atr = WildersAverage(TrueRange(high,  close,  low), length);
    df['H-L'] = df['High'] - df['Low']
    df['H-C1'] = df['High'] - df['Close'].shift()
    df['C1-L'] = df['Close'].shift() - df['Low']
    df['TrueRange'] = df[['H-L','H-C1','C1-L']].max(axis=1)
    df['WildersATR'] = df['TrueRange'].ewm(alpha=1.0 / params['length'], adjust=False).mean()

    # def diff = hlc3 - hlc3[1];
    df['Diff'] = ((df['High'] + df['Low'] + df['Close']) / 3) - ((df['High'].shift() + df['Low'].shift() + df['Close'].shift()) / 3) # Forward peak here?

    # def vp = Sum(if diff > factor * atr then volume else 0, length);
    df['VP_Helper'] = np.where(df['Diff'] > params['factor'] * df['WildersATR'], df['Volume'], 0)
    df['VP'] = df['VP_Helper'].rolling(params['length']).sum()

    # def vn = Sum(if diff < -factor * atr then volume else 0, length);
    df['VN_Helper'] = np.where(df['Diff'] < -params['factor'] * df['WildersATR'], df['Volume'], 0)
    df['VN'] = df['VN_Helper'].rolling(params['length']).sum()

    # plot VPN = ExpAverage(100 * (vp - vn) / Sum(volume, length), emaLength);
    df['RollingVol'] = df['Volume'].rolling(params['length']).sum()
    df['VPN'] = talib.EMA(100 * (df['VP'] - df['VN']) / df['RollingVol'], timeperiod=params['emaLength'])

    # plot VPNAvg = MovingAverage(averageType, VPN, averageLength);
    if params['averageType'] in ['simple','sma','SMA','SIMPLE']:
        df['VPNAvg'] = talib.SMA(df['VPN'], timeperiod=params['averageLength'])
    
    # plot CriticalLevel = criticalValue;
    df['CriticalLevel'] = params['criticalValue']

    # VPN.DefineColor("Above", Color.UPTICK);
    # VPN.DefineColor("Below", Color.DOWNTICK);
    # VPN.AssignValueColor(if VPN > CriticalLevel then VPN.Color("Above") else VPN.Color("Below"));
    # VPNAvg.SetDefaultColor(GetColor(7));
    # CriticalLevel.SetDefaultColor(GetColor(1));

    # Gimicks, don't need the top bit for now

    return df


params = {
    "length": 30,
    "emaLength": 3,
    "averageLength": 30,
    "factor": 0.1,
    "criticalValue": 10,
    "averageType": "simple"

}

# Import a 1min dataset and rename columns as necessary
df = pd.read_csv("SPY.csv").iloc[-2000:,:]
df['time'] = pd.to_datetime(df['time'])
df = df.set_index('time')
df = df.rename(columns={'open':'Open', 'high':'High', 'low':"Low", "close": "Close", "volume": "Volume"})
df = VPN_Indicator(df, params)

# Plot the results
apds = [ mpf.make_addplot((df['CriticalLevel']), panel=2, color='g'),
         mpf.make_addplot((df['VPN']), panel=2, color='g'),
         mpf.make_addplot((df['VPNAvg']), panel=2, color='g'),
       ]

mpf.plot(df[['Open', 'High', 'Low', 'Close', 'Volume']], addplot=apds, figscale=1.2, volume=True)

...这导致一个看起来像这样的情节：

mpfplot

... 这很接近，但峰值与 ThinkOrSwim 图不一致。所以我想从懂这些语言的人那里知道我可能会离开哪里？谢谢！

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