Algo trading software for Zerodha Kiteconnect api
Budget: ₹12,500 – ₹37,500 INR
Here are my requirements for algo trading application for Zerodha's Kiteconnect api.
1. Live-trading Python application for intraday trading based on a '5min' candle.
2. Indicators: MACD (8,17,5), RSI(14), ATR(7) and ADX(10,10)
3. I'm using a scoring model for buy/sell orders for 'long' only strategy. Here's how it works:
MACD > Signal = 10 points
MACD > 0 = 10 points
RSI > 50 = 10 points
ADX> 25= 10 points
DMI plus> DMI minus= 10 points
When the conditions aren't met, the points will be equal to zero for the respective indicator.
4. Buy when score[prev candle] < 40 and score[present candle] >= 40.
If score >= 40 and <50, Sell when LTP > Buy Price + 1*ATR
If score >= 50, Sell when LTP > Buy Price + 1.5*ATR
For Stoploss, Sell when LTP < Buy Price - 1.5*ATR or score<20
5. The score needs to be calculated real-time. Hence, the OHLC candles should be formed real-time, and not after the candle formation is completed.
6. Database: Sqlite3 or Postgresql for storing tick data. Python for logic. Pandas dataframes for reading ticks from sql, storing candles, indicator calculations, scores, positions and order info.
7. I may change the stocks to trade everyday or intraday. Hence I should be able to retrieve historical candle data from Kite historical api and append the newly forming candles to the historical candles.
8. I need to do backtesting at tick level data. Nice to have a GUI application to login, select tickers to trade from a list, close the trade manually (if required) and update 'position info' without having to stop the application.
I would like to know the charges and timeframe for completing the project.
1. Live-trading Python application for intraday trading based on a '5min' candle.
2. Indicators: MACD (8,17,5), RSI(14), ATR(7) and ADX(10,10)
3. I'm using a scoring model for buy/sell orders for 'long' only strategy. Here's how it works:
MACD > Signal = 10 points
MACD > 0 = 10 points
RSI > 50 = 10 points
ADX> 25= 10 points
DMI plus> DMI minus= 10 points
When the conditions aren't met, the points will be equal to zero for the respective indicator.
4. Buy when score[prev candle] < 40 and score[present candle] >= 40.
If score >= 40 and <50, Sell when LTP > Buy Price + 1*ATR
If score >= 50, Sell when LTP > Buy Price + 1.5*ATR
For Stoploss, Sell when LTP < Buy Price - 1.5*ATR or score<20
5. The score needs to be calculated real-time. Hence, the OHLC candles should be formed real-time, and not after the candle formation is completed.
6. Database: Sqlite3 or Postgresql for storing tick data. Python for logic. Pandas dataframes for reading ticks from sql, storing candles, indicator calculations, scores, positions and order info.
7. I may change the stocks to trade everyday or intraday. Hence I should be able to retrieve historical candle data from Kite historical api and append the newly forming candles to the historical candles.
8. I need to do backtesting at tick level data. Nice to have a GUI application to login, select tickers to trade from a list, close the trade manually (if required) and update 'position info' without having to stop the application.
I would like to know the charges and timeframe for completing the project.