Day Trader: Crypto Pattern Trading in High-Volatility Markets (TA)

This robot was designed for active day traders who specialize in crypto trading and prefer using traditional market analysis methods enhanced with the latest advancements in machine learning technology. They are unafraid of heightened market volatility.

The typical trade duration is just 5 hours, with a maximum of 32 open trades at any given time. Coupled with an average success rate of 69% for trades, this robot proves to be an effective tool for crypto day traders.

The foundation of this robot's algorithm relies on trading classical technical analysis patterns that thrive in conditions of increased volatility, such as "Broadening Bottom" and " Broadening Wedge Descending," among others. These patterns have been established for many years, but most traders lacked the means to effectively identify them and, more importantly, integrate them appropriately. Utilizing AI, Tickeron's quantitative team successfully addressed this issue, crafting the most precise algorithm for detecting patterns as they emerge and the optimal approach for combining various pattern types.

Following the initiation of a trade, a "Take Profit" order is placed at the target price level determined by machine learning algorithms. Additionally, this algorithm pinpoints the moment when a pattern is considered expired, and the trade will be closed if the target price is not reached. Multi-level backtests indicate that this approach enables the robot to close profitable trades as swiftly as possible.

Additionally, we offer our users a manual way to determine the level of exit from a trade that is acceptable to them. On the "Open Trades" tab, for each trade, the profit amount is highlighted in a certain color. If the “Profit” column is gray, this means that the loss on the position is moderate and does not exceed 5%. If the color is red, then the risk is already significant and exceeds 5%. This makes it easy to visually determine the overall value of a risk-based on open trades.

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