VPN Indicator ThinkScript to Python

Question

Taking a stab at converting a ThinkScript to Python for the first time, and I think my logic is right, but I am missing something as the two plots for the indicator don't match.

Trying to convert the ThinkScript for the VPNIndicator to a Python implementation. Looking for someone knowledgeable in both languages to contribute here.

To start, the indicator plot in ThinkorSwim looks like this (bottom):

So I'm trying to replicate that plot using matplotlib finance, but first I need to translate from ThinkScript to Python, which I've attempted here:

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)

... which results in a plot that looks like this:

... which is close, but the peaks don't line up with the ThinkOrSwim plot. So I'm wanting to know from someone who knows these languages where I might be off? Thanks!

The VPN curves clearly have different values although their overall shapes are similar. I would suggest looking very carefully at the actually numbers coming out of ***each and every one of the intermediate calculation steps*** comparing the values from the ThinkScript to those from the Python script. It seems a reasonable guess that one or more of the intermediate steps is somewhat off. See if you can narrow down which one(s) is(are) off. — Daniel Goldfarb, Nov 17 '21 at 03:38
I believe some Thinkorswim function is implemented in python slightly different therefore resultant graph is close. I could determine the one running tests with `SPY.csv`. — artscan, Nov 17 '21 at 05:01
I also just noticed that the volume data that I have in SPY.csv is different. Albeit close but not an identical match to TOS’s charts. I’m going to follow the advice of checking each data point carefully, even modify the thinkscript to plot the data points in ToS and see how I do. Thanks! — wildcat89, Nov 17 '21 at 05:31

score 1 · Accepted Answer · answered Apr 21 '22 at 23:52

Try using this to calculate ATR. This gives the same output as TOS.

import numpy as np

def ema(arr, periods=14, weight=1, init=None):
    leading_na = np.where(~np.isnan(arr))[0][0]
    arr = arr[leading_na:]
    alpha = weight / (periods + (weight-1))
    alpha_rev = 1 - alpha
    n = arr.shape[0]
    pows = alpha_rev**(np.arange(n+1))
    out1 = np.array([])
    if 0 in pows:
        out1 = ema(arr[:int(len(arr)/2)], periods)
        arr = arr[int(len(arr)/2) - 1:]
        init = out1[-1]
        n = arr.shape[0]
        pows = alpha_rev**(np.arange(n+1))
    scale_arr = 1/pows[:-1]
    if init:
        offset = init * pows[1:]
    else:
        offset = arr[0]*pows[1:]
    pw0 = alpha*alpha_rev**(n-1)
    mult = arr*pw0*scale_arr
    cumsums = mult.cumsum()
    out = offset + cumsums*scale_arr[::-1]
    out = out[1:] if len(out1) > 0 else out
    out = np.concatenate([out1, out])
    out[:periods] = np.nan
    out = np.concatenate(([np.nan]*leading_na, out))
    return out


def atr(highs, lows, closes, periods=14, ema_weight=1):
    hi = np.array(highs)
    lo = np.array(lows)
    c = np.array(closes)
    tr = np.vstack([np.abs(hi[1:]-c[:-1]),
                    np.abs(lo[1:]-c[:-1]),
                    (hi-lo)[1:]]).max(axis=0)
    atr = ema(tr, periods=periods, weight=ema_weight)
    atr = np.concatenate([[np.nan], atr])
    return atr

VPN Indicator ThinkScript to Python

1 Answers1