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Deepak
- Rate L872
- Response 17h
-
Students28
Number of students Deepak has accompanied since arriving at Superprof
Number of students Deepak has accompanied since arriving at Superprof
L872/hr
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- Maths
- Algorithms
- Computing
How to perform Algo trading in python. how to trade automatically on any broker platform like zerodha
- Maths
- Algorithms
- Computing
Lesson location
About Deepak
i have build several auto trading app in python , c++. i have coded many indicators like fractal , renko, itchimoko , supertrend etc. you can learn how to trade without adding Ur sentiments.
profession coder in c++ , python with understanding in strategy
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- Primary
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- +9
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- English
All languages in which the lesson is available :
English
Algorithmic trading is the process of using computer algorithms to automate the process of buying and selling financial securities. Python is a popular programming language used for developing trading algorithms due to its ease of use, versatility, and rich ecosystem of libraries and tools. Here are some steps you can follow to develop an algorithmic trading strategy using Python:
Choose a brokerage or trading platform that supports algorithmic trading with Python. Some popular options include Interactive Brokers, TD Ameritrade, and Robinhood.
Familiarize yourself with financial data analysis and visualization libraries such as Pandas, NumPy, Matplotlib, and Seaborn. These libraries will help you to analyze and visualize financial data to identify patterns and develop trading strategies.
Learn how to access real-time market data using APIs provided by your chosen brokerage or data provider. Most financial data providers offer APIs that allow developers to retrieve real-time and historical market data programmatically.
Develop your trading strategy by using Python to implement your algorithmic trading logic. You can use a variety of techniques such as technical analysis, machine learning, and statistical analysis to identify profitable trading opportunities.
Backtest your strategy using historical data to evaluate its performance. You can use libraries such as Backtrader, PyAlgoTrade, or Zipline to backtest your strategy and simulate trading in a historical market environment.
Deploy your strategy to the live market once you have tested and refined your trading strategy. Make sure to monitor your strategy's performance and adjust it as needed based on market conditions.
It's worth noting that algorithmic trading carries risks and requires a deep understanding of financial markets, trading strategies, and programming. It's essential to thoroughly test your strategy before deploying it in a live trading environment and to use risk management techniques such as position sizing, stop-loss orders, and diversification to minimize your exposure to market risk.
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Rates
Rate
- L872
Pack prices
- 5h: L4360
- 10h: L8720
online
- L872/h
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