TII (trend intensity index) indicator

APIs, Automation, Algos & Code
1

How can I define or implement a TII (trend intensity index) indicator inside the algo?

2

Hi @sunita_sriv
can you share the pine script / math formula for TII.

If we can get the formula, we can simply call the historical data, and apply the function for TII on it to get its values

3

Hi,

Thank you for your reply. Please find the formula below.

image

4

Hi @sunita_sriv ,

Do run the following commands in the cmd -

pip install dhanhq==2.2.0
pip install Dhan-Tradehull==3.3.1

Refer the code for the indicator -

from Dhan_Tradehull import Tradehull
client_code = ""
tsl = Tradehull(client_code, mode="pin_totp", pin="", totp_secret="")

def calculate_ti_indicator(df, period=28, close_col="close"):
    """
    TI Indicator calculation based on:
    SMA_28 = 28-period Simple Moving Average of Close
    SD+ = Sum(Close - SMA_28) when Close > SMA_28
    SD- = Sum(SMA_28 - Close) when Close < SMA_28
    TI1 = SD+ / (SD+ + SD-) * 100
    """

    df = df.copy()

    # 1. SMA 28
    df[f"SMA_{period}"] = df[close_col].rolling(window=period).mean()

    # 2. Positive deviation
    df["SD_plus_value"] = 0.0
    df.loc[df[close_col] > df[f"SMA_{period}"], "SD_plus_value"] = (df[close_col] - df[f"SMA_{period}"] )

    # 3. Negative deviation
    df["SD_minus_value"] = 0.0
    df.loc[df[close_col] < df[f"SMA_{period}"], "SD_minus_value"] = (df[f"SMA_{period}"] - df[close_col])

    # 4. Rolling sum of SD+ and SD-
    df["SD_plus"] = df["SD_plus_value"].rolling(window=period).sum()
    df["SD_minus"] = df["SD_minus_value"].rolling(window=period).sum()

    # 5. Final TI indicator
    total_sd = df["SD_plus"] + df["SD_minus"]

    df["TI1"] = 0.0
    df.loc[total_sd != 0, "TI1"] = (df["SD_plus"] / total_sd) * 100

    return df

hist_data = tsl.get_historical_data(tradingsymbol="NIFTY",exchange="INDEX",timeframe="5")

print(hist_data)
hist_data = calculate_ti_indicator(hist_data, period=28)

print(hist_data)

Output -

Screenshot 2026-06-30 170933

5

Thank you so much @Tradehull_Imran

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