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<li>Develop your Strategy: Write your strategy in python, using <a href="https://pandas.pydata.org/">pandas</a>. Example strategies to inspire you are available in the <a href="https://github.com/freqtrade/freqtrade-strategies">strategy repository</a>.</li>
<li>Download market data: Download historical data of the exchange and the markets your may want to trade with.</li>
<li>Backtest: Test your strategy on downloaded historical data.</li>
<li>Optimize: Find the best parameters for your strategy using hyperoptimization which employs machining learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.</li>
<li>Optimize: Find the best parameters for your strategy using hyperoptimization which employs machine learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.</li>
<li>Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.</li>
<li>Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).</li>
<li>Run using Edge (optional module): The concept is to find the best historical <a href="edge/#expectancy">trade expectancy</a> by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.</li>