Launched this week
TradeStaq
The trading bot you describe. Not the one off-the-shelf.
12 followers
The trading bot you describe. Not the one off-the-shelf.
12 followers
TradeStaq is an automated trading platform that lets anyone — from complete beginners to professional quant traders — build, test, and deploy trading bots on crypto exchanges without writing code or managing servers. The core idea: you describe what you want your bot to do in plain English, and our AI (FORGE) writes the strategy code, backtests it against years of historical data, and deploys it to your exchange account — all in minutes.
How does FORGE handle strategy logic that needs real-time market context like funding rates or liquidation data across multiple exchanges, or is it mostly working off price and volume candles?
@diyarzeytushwa Good question — it's more than candles, and I'll be precise about where the line is today.
What a strategy can read right now: OHLCV candles, live ticker + spread, the order book with bid/ask imbalance, market sentiment (Fear & Greed), a news feed with sentiment, and your own position data — including its liquidation price. So you can gate entries on order-flow imbalance, sentiment shifts, or news events, not just price and volume.
Where the honest edge is: funding rate, open interest, and mark/index price are modeled in our futures engine but not yet surfaced to custom strategies — and cross-exchange / market-wide liquidation data isn't in the strategy context (a strategy runs on the exchange you connect; you get your own position's liq price, not a market-wide liquidation feed).
Surfacing funding/OI to strategies is exactly what we'll prioritize on demand. If that's a strategy you'd build, tell us and it jumps the queue — what would you key off, funding flips or OI divergence?
Check our full market data info here:
https://www.tradestaq.com/docs/custom-strategies/market-data)
The plain English to strategy code pipeline sounds like the real win here, that's a lot of plumbing that usually scares people off. Curious how flexible FORGE gets once you want to layer in custom indicators or risk rules beyond the basics.
@sevgivnzu
This is the fun part — FORGE isn't capped at presets, because what it generates is real strategy code against our `td.*` API. "Beyond the basics" just means writing more code (or asking FORGE to write it).
Custom indicators: you get the built-ins (RSI, MACD, Bollinger, etc.), custom-period variants like td.indicators.rsiCustom(period), and the full raw OHLCV arrays — so any indicator you can compute, you can build. Multi-timeframe and persistent state across bars are supported too.
Custom risk: two layers. The platform enforces stop-loss / take-profit as a hard floor — a bot can't silently stop protecting a position. On top of that you write your own risk logic (position sizing, max exposure, trailing stops, per-bot balance allocation), all exposed as tunable parameters.
So it scales cleanly: plain English → generated code → drop into the code yourself when you want full control. No ceiling at "the basics."
Full td.* API (indicators, market data, state, execution): https://www.tradestaq.com/docs/custom-strategies/td-api-reference
Curious how FORGE handles edge cases or strategy logic that is ambiguous on purpose, like "buy the dip" — does it ask for clarification or just make assumptions and run with them?
@devransulb15560 FORGE doesn't interrogate you first — it assumes, but it never hides the assumption. this is actually deliberate so she doesn't turn an advisor instead of an executor.
For something like "buy the dip," it picks a sensible interpretation and turns every ambiguous choice into a labeled, tunable parameter with a default — a "dip %" (say 5%), a lookback window, an RSI threshold — and hands you working code right away, instead of making you answer 20 questions before you see anything.
Two things that keep that honest: it's conversational, so you can just say "no, I mean a 5% drop over 1h" and it adjusts; and every assumption is a parameter you can see, change, and backtest — so you prove the interpretation on historical data before any real money, rather than trusting a black-box guess.
So: assume + expose + let you tune, not clarify-then-build. The parameters and the backtest are the safety net.
Check our docs on Forge prompting here: https://www.tradestaq.com/docs/ai-builder/overview
How does FORGE actually decide which exchange APIs it works with, and can I run multiple strategies on the same account without them stepping on each other's trades?
@elasezik6181 Great question 🙌
On exchanges: FORGE itself is exchange-agnostic — it writes the strategy logic against a normalized market-data API, so the strategy doesn't care where it runs. What determines compatibility is which exchange you connect. We support 15+ exchanges through one unified integration — Binance, Bybit, OKX, Bitget, Hyperliquid, Kraken, Coinbase, KuCoin, Gate.io, MEXC, BingX, and more.
Connect your keys, deploy the bot there. Full list + the exact API permissions to set: https://www.tradestaq.com/docs/exchanges/overview
On multiple strategies on one account: yes, it's built for it. Each bot gets its own balance allocation — you cap it to a % of the account — so two strategies never over-commit the same capital, and each bot manages its own orders and risk independently. One honest tip: give each bot its own slice, and ideally distinct pairs — two bots on the exact same symbol/account will share the exchange's netted position, so isolate by allocation or symbol.
Config + risk controls: https://www.tradestaq.com/docs/trading-bots/risk-management
Would love your feedback if you take it for a spin 🚀
how does FORGE handle edge cases or ambiguous strategy descriptions when you throw something vague at it, and does it flag potential issues before deployment?
@bektemirfe46994 FORGE doesn't stall on you — it takes a sensible interpretation and turns the fuzzy parts into labeled, tunable parameters with defaults (a "dip" becomes a % drop + timeframe + threshold you can see and change), then hands you working code. It's conversational, so you refine in plain English ("make it 5% over 1h").
And it's instructed to always include stop-loss / take-profit logic even if you didn't ask — so a vague description doesn't ship without a safety exit.
Flagging issues before deploy: yes. There's a validation pass that runs before you go live — it confirms the code compiles, checks the strategy actually has exit logic (a close / stop-loss / take-profit, so a strategy with "no way out" gets flagged rather than deployed), and runs a quick backtest to surface runtime errors. On top of that you backtest + paper-trade first, and at runtime the platform enforces stop-loss/take-profit as a hard floor.
So: it assumes rather than interrogates, but it won't let you ship code that can't exit or won't run.
Validation + testing: https://www.tradestaq.com/docs/custom-strategies/testing-validation
Tried it yesterday and the plain English to strategy code thing actually works. Wrote a basic RSI bot, backtested over two years of data, and had it running on my Binance account before lunch.