We’ve been working on Nvestiq, a platform that lets traders go from an idea to a fully automated trading bot without writing code.
Most retail traders face three walls:
Technical barrier — serious backtesting usually requires Python, Pine Script, or MQL5.
Validation barrier — without robust simulation, it’s hard to separate luck from a real edge.
Deployment barrier — even validated strategies often die before they get translated into bug-free scripts.
We’re trying to remove all three. The workflow looks like this:
Describe your idea in plain English. Our semantic parser interprets abstract concepts (like supply/demand zones or liquidity grabs) and turns them into testable logic.
Run institutional-grade backtests. High-fidelity simulations provide detailed metrics and visualizations.
Deploy with one click. The system compiles the strategy into clean Pine Script or MQL5 for live execution.
We ran an invite-only beta earlier this year, gathered feedback, and have rebuilt core parts of the engine. Next week we’re opening up our second free beta and are looking for more quants and traders to test it.
Our aim isn’t to replace Python, R, or other quant stacks. Instead, we want to provide a bridge for discretionary and semi-systematic traders who don’t have the bandwidth to code everything from scratch but still want rigorous testing and automation.
Would love feedback from this community on:
Which validation methods would you consider essential?
How you’d want to see risk management and reporting handled?
Any concerns about using natural language for strategy definition?
Aman4312•5mo ago
Most retail traders face three walls:
Technical barrier — serious backtesting usually requires Python, Pine Script, or MQL5.
Validation barrier — without robust simulation, it’s hard to separate luck from a real edge.
Deployment barrier — even validated strategies often die before they get translated into bug-free scripts.
We’re trying to remove all three. The workflow looks like this:
Describe your idea in plain English. Our semantic parser interprets abstract concepts (like supply/demand zones or liquidity grabs) and turns them into testable logic.
Run institutional-grade backtests. High-fidelity simulations provide detailed metrics and visualizations.
Deploy with one click. The system compiles the strategy into clean Pine Script or MQL5 for live execution.
We ran an invite-only beta earlier this year, gathered feedback, and have rebuilt core parts of the engine. Next week we’re opening up our second free beta and are looking for more quants and traders to test it.
Our aim isn’t to replace Python, R, or other quant stacks. Instead, we want to provide a bridge for discretionary and semi-systematic traders who don’t have the bandwidth to code everything from scratch but still want rigorous testing and automation.
Would love feedback from this community on:
Which validation methods would you consider essential?
How you’d want to see risk management and reporting handled?
Any concerns about using natural language for strategy definition?
Demo and beta sign-up: nvestiq.com