How to use or implement Opening Range Breakout Strategy with EMA in real market

Which version are you running? The lastest version is on Github. Pip is for major releases.

import pandas_ta as ta
print(ta.version)

0.1.97b

Upgrade.

$ pip install -U git+https://github.com/twopirllc/pandas-ta

Is your feature request related to a problem? Please describe. Not exactly. I want to understand how to use pandas_ta for opening range or time range breakout strategies in real market. Is it only for technical analysis or can be used in real market.

Describe the solution you’d like Some example in real market suppose I want to use it for Indian broker like Zerodha/AliceBlue/Samco who provide API and tick data. I want to create a simple opening range breakout strategy for first 15min or 30min or 1hr time. But at the same time want to check whether LTP is above EMA 21, how can I do it using pandas_ta? How to create this custom strategy and where I should use the broker API when I get a signal. I saw the jupyter notebooks there is too much information and it confuses me a lot. Simple examples can be helpful.

Thanks for using Pandas TA!

[rahulmr]

So finally I started working on this long pending stuff. Here is my code

import yfinance as yf
import pandas_ta as ta

import schedule

sbin = yf.Ticker("SBIN.NS")

def place_order(order_type):
    print(f"Place {order_type} order")

def run_strategy():
    df = sbin.history(period="3d", interval="15m")
    print(df)
    df.ta.cdl_inside(append=True)
    df.ta.vwap(append=True)
    print(df)
    condition = df.iloc[-1]['CDL_INSIDE'] == 1 and df.iloc[-1]['VWAP'] > df.iloc[-1]['close']
    if condition:
        place_order('buy')

# This is expected to run every 15 minutes (some 10-15 seconds like 9:30:10 AM IST) 
# Yfinance provides data with some delay - broker data will not have that delay
schedule.every(interval=15).minutes.do(run_strategy)



# df = sbin.history(period="3d", interval="15m")
# print(df)
# df.ta.cdl_inside(append=True)
# df.ta.vwap(append=True)
# print(df.iloc[-1]['CDL_INSIDE'])
# print(df.iloc[-1]['VWAP'])
# ## TODO Add EMA 7 and EMA 21 , can we also get Pivot Points Fibonacci ?
## I also have thoughts on how can we create a simulation of previous days
# Like getting data by adding 15 minutes to it. That is another part though.
# creating a watchlist of Indian Stocks and applying this.

[twopirllc]

Hello @rahulmr,

Just came across hackingthemarkets: Fullstack Trading App repo. It contains an ORB Strategy (backtest.py) but using Alpaca API (import alpaca_trade_api) for trade execution. It may be a good starting point to decompose and replace Alpaca with your broker. Of course you will need to forward test on a paper/demo account and adjust it for live execution.

Kind Regards, KJ