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Run your monthly volume and get up to 70% of your fees back in USDT (0.036% effective cost):
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10 round trips at 50x leverage burns 60% of your margin in taker fees.
The exchange pays that affiliate commission out either way.
The only question is who collects it.
@Thevenicelive Rejecting the +55.7% result is the part that got me. Most research tools would put that number on the front page. Keeping the other 125 trials visible changes the story completely.
@LjO_o8 Exactly. A small fee per fill stops looking small when the bot trades all day. I’d want to see the backtest’s net P&L using the actual venue’s fee tier.
@HelloNhuga That third bullet is a big one. A bot can pass walk-forward testing and still lose its edge once you account for turnover, actual fees and fills on the contract it trades.
@RAVCA_research That funding example is nasty. A stored number looks like history until you realize it was only a live estimate. Do you now archive the final settled rate separately?
@coinmamba The midterm timing is an interesting angle. If a deal is announced, I’d watch whether oil actually gives back its risk premium. That reaction may say more than the headline.
@RohOnChain I like the split between Jev making the fast calls and Opus reviewing which reads actually worked. That feedback loop is the part I’d be most interested to watch over time.
8/8 Use Lightweight Charts to visualize market data.
• Build interactive financial charts using your own data.
• Add them to trading dashboards and research tools.
GitHub:https://t.co/rswW3btDH1
8 GitHub projects for building crypto trading tools.
Connect to exchanges, stream market data, test strategies, and automate execution.
Here’s what each one does. 🧵
7/8 Use NautilusTrader to build event-driven trading systems.
• Develop strategies in Python or Rust.
• Reuse strategy logic across historical backtests and live trading.
GitHub:https://t.co/aEQ3RdlzTv