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In modern algorithmic trading and market analytics, applying technical indicators on real-time intraday data is crucial for making fast, informed decisions. In this step-by-step guide we explain how to implement technical indicators on realtime intraday data and at the end you’ll be able to:

• Fetch real-time intraday stock data
• Apply indicators like EMA, MACD, and VWAP
• Detect crossover conditions for trade signals
• Print alerts when trade conditions are met

We’ll walk through the Python script, explain each part, and show how it works together to analyze live data using the pandas_ta library.

Prerequisites

Before we begin, please make sure you have installed Python 3.8+ with the required libraries:

pip install pandas pandas_ta

Also ensure you have access to a module (or class) named MControlAPI for fetching intraday data. You can learn make this module or get this from my post “Fetch Intraday Data of Stocks for Any Interval Period using Python“.

Overview of the Script

This script does the following:

1. Fetches real-time intraday data for a given stock (e.g., SBIN)
2. Renames and formats the columns for compatibility with indicators
3. Applies a strategy with indicators using pandas_ta
4. Evaluates signal conditions for MACD and EMA crossovers
5. Prints alerts when a trade signal is detected

Step-by-Step Breakdown

Step 1: Import Required Libraries

import pandas_ta as ta
import pandas as pd
from time import sleep
from intraday import MControlAPI

• pandas_ta: For technical indicators like EMA, MACD, VWAP
• pandas: For working with tabular data
• sleep: To create a delay between data fetches
• MControlAPI: Custom class to fetch intraday data

Step 2: Initialize Flags and the Data Fetcher

macd_ok = False
ema_ok = False
obj = MControlAPI('SBIN')
nd = 0

• macd_ok, ema_ok: Track if trade conditions are met
• MControlAPI('SBIN'): Initializes the data fetcher for SBIN

Step 3: Fetch Real-Time Data in a Loop

while nd > -1:
    nd = obj.fetch_intraday_data()
    ...
    sleep(obj.delta_time)

• Continuously fetches new intraday data
• sleep(obj.delta_time): Ensures polling interval isn’t too aggressive

Step 4: Format the DataFrame

df = obj.dataframe.copy()
df.rename({'h': 'high', 'l': 'low', 'c': 'close', ...}, axis=1, inplace=True)
df.set_index(pd.DatetimeIndex(df['dt']), inplace=True)

• Renames columns to match pandas_ta requirements
• Sets the datetime as index for proper time-series behavior

Step 5: Define and Apply a Custom Strategy

customStrategy = ta.Strategy(
    name='My Custom Strategy',
    description='EMA 9, 29, cross over with MACD and vwap support',
    ta=[
        {'kind': 'ema', 'length': 9},
        {'kind': 'ema', 'length': 26},
        {'kind': 'vwap'},
        {'kind': 'macd', 'fast': 12, 'slow': 26, 'col_names': ('MACD', 'MACD_H', 'MACD_S')}
    ]
)
df.ta.strategy(customStrategy, timed=True)

• Combines EMA (Exponential Moving Average), MACD, and VWAP into one strategy
• Applies the strategy to the dataframe using pandas_ta

Step 6: Analyze the Indicators

prev = df.iloc[-2]
curr = df.iloc[-1]

• Grabs the latest and previous data points to compare values for crossovers

Step 7: Signal Detection Logic

if macd_ok == False and prev['MACD'] < curr['MACD'] and prev['MACD_S'] < curr['MACD_S']:
    macd_ok = True
...
if ema_ok and macd_ok and curr['VWAP_D']/curr['close'] < 1.02 and curr['MACD_S'] > curr['MACD']:
    print('It is high time to make a position')

• Detects MACD and EMA crossovers
• Checks if the price is close to VWAP
• Triggers a buy signal if all conditions are met

Step 8: Market Close Check

last = obj.dataframe.iloc[-1]['dt']
if (last.hour + last.minute/60) > 15.5:
    print('Market is closed')
    break

• Stops execution if the current time is after 3:30 PM

The Complete Source Code

Output Example

When run, the script prints the most recent row with the calculated indicators and may show:

It is high time to make a position

This helps automate signal detection without manual chart checking.

Ideas to Extend This Script

• Add more indicators like RSI, Bollinger Bands
• Send alerts via email or Telegram
• Execute simulated or real trades via broker APIs
• Log signals to a CSV or database

Conclusion

Implementing technical indicators on real-time intraday data using Python is a game-changer for any trader or developer looking to build intelligent trading systems.

This guide demonstrated how to:

• Fetch live stock data
• Apply powerful technical indicators using pandas_ta
• Detect and act on crossover conditions like EMA and MACD
• Trigger alerts when a trade setup is formed

With just a bit of customization, you can adapt this to any stock or strategy. Start experimenting and take your intraday analysis to the next level!

If you are interest in more guides related to trading, you can visit here trading.

And for more python guides visit here Python.

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