Hi all,
I wanted to share a project I’ve been developing for my own trading over the past couple of years, and hopefully get some constructive feedback from others who are into systematic and AI-assisted trading.
I trade short- to medium-term crypto futures and my biggest challenge early on was filtering out market noise and getting clear, rules-based signals without the emotional bias that creeps in when trading manually. That led me to start building TradelQ, a Telegram-based AI trading assistant.
The system is designed to:
For transparency, it’s still under active development - current version is 0.13 Beta - and I’ve been logging changes, feature additions, and performance tweaks as I go. While it started as a personal tool, I’ve recently opened it to a small group of other traders to see how it performs in different hands and get feedback on the logic.
My main goals now are:
If anyone here is working on similar automation projects, has experience with AI-assisted strategies, or just wants to talk shop about indicator combinations and trade filtering, I’d be interested in comparing notes.
Happy to answer any questions about the build, reasoning behind the strategy stack, or the results I’ve seen so far.
I wanted to share a project I’ve been developing for my own trading over the past couple of years, and hopefully get some constructive feedback from others who are into systematic and AI-assisted trading.
I trade short- to medium-term crypto futures and my biggest challenge early on was filtering out market noise and getting clear, rules-based signals without the emotional bias that creeps in when trading manually. That led me to start building TradelQ, a Telegram-based AI trading assistant.
The system is designed to:
- Scan a basket of popular crypto pairs (BTC, ETH, BNB, SOL, DOGE, XRP, etc.) in real time.
- Run them through a combination of technical frameworks:
- ICT concepts (Fair Value Gaps, Market Structure Shifts, liquidity sweeps)
- VWAP (session-based volume targeting)
- MACD, Stochastic RSI (3,3,14,14), and Bollinger Bands
- On-Balance Volume, volume spikes, and RSI divergence detection
- Produce trade ideas that include entry, stop-loss, take-profit, trade direction, and a confidence rating - all with an AI-generated reasoning section explaining the setup.
For transparency, it’s still under active development - current version is 0.13 Beta - and I’ve been logging changes, feature additions, and performance tweaks as I go. While it started as a personal tool, I’ve recently opened it to a small group of other traders to see how it performs in different hands and get feedback on the logic.
My main goals now are:
- Keep refining the indicator fusion logic and confidence scoring.
- Add more robust backtesting on historical data with DST and timezone accuracy.
- Continue to reduce false positives without missing valid high-R setups.
If anyone here is working on similar automation projects, has experience with AI-assisted strategies, or just wants to talk shop about indicator combinations and trade filtering, I’d be interested in comparing notes.
Happy to answer any questions about the build, reasoning behind the strategy stack, or the results I’ve seen so far.
