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Markov chains are really helpful in dealing with randomness. You can pretty much tweak any random process to fit or be seen as a Markov chain, even trading.

Read this before building your trading algorithm. A trading algorithm is an idea that has been brought to life and automated with codes. When you’re about to start building a trading algorithm, learn about successful trading strategies. You’ll be turning these ideas into an

The market doesn’t care about fancy theories. It wants cold, hard proof. Backtests are the first line of the brawl, the proving ground where you take models and see if they can perform well historically. Not all ideas make it out, but the survivors are worth a shot.

Don't worry about the trigger...... The algorithm will handle it. Just focus on the target.

Take it easy on yourself. It is quite difficult to produce a backtest that is 100% accurate. This is, in my opinion, one of the most crucial things algotrader should know. After that, you may actually begin looking for a viable strategy.

Quick response times are essential in algorithmic trading. Don't waste your time on a strategy if you can't test it with the statistical value of more than 100 transactions in two weeks. Months will pass before you can validate this.

These books will help you to understand options pricing and financial machine learning without the headache even if you're new to the whole options game.

You don't know what is ahead. Instead of depending on chance, try to study as much as possible about the market. Create and implement a wide range of strategies.

Take academic papers with a grain of salt, but don't dismiss them entirely. One paper will not yield a viable strategy, but combining lessons from 20 publications may.

If you're an algo trader, this research is worth a look. They trained models on real stock data to make profitable trades. The results show that AI can learn trading strategies in this complex environment.

QF-lib (QuarkFin) is a financial Python 🐍 library developed by CERN. It has a range of features including backtesting, portfolio construction, time series analysis, risk monitoring, and financial charting. Link to the GitHub repo: github.com/quarkfin/qf-lib

Most algorithmic traders overlook a two-step basic approach that can solve practically any issue. 1. Keeping track of your data 2. Reviewing the data

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Daily habits are what makes a person who they are

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