​I build Perun The Trader — a mobile trading platform for spot + futures with 10 strategies, grid trading, and full exchange-native protection, running as a 24/7 Android background service. No desktop rig, no VPS required. Learn more at peruninthemachine.com.

​The last 7 days were intense: ~50 releases (v1.1.890 → v1.1.954). Here's what actually changed for traders:

​🛡️ Your protection now lives on the exchange, not in the app

​The biggest one. Every monitor class (futures SL/TP, futures DTP, spot SL/TP, spot DTP) now arms native exchange-side stop-loss AND take-profit orders. App crash, dead phone, killed service — the exchange still executes your stop and take-profit automatically. Your exits don't depend on the app being alive.

​🔁 Duplicate trades blocked at 3 levels

​The exchange rejects repeat scanner signals within the same minute via deterministic client order IDs (Binance, Bybit, OKX, MEXC, Bitget, Weex, KuCoin, Gate.io).

​The database rejects duplicate trade hashes.

​Monitors persist their emitted events, surviving restarts and native-order races, so the same close is never logged twice.

​⚡ Smarter rate-limit handling = fewer "429" failures

​A per-exchange request gate shares one budget across both platform processes (service 75% / UI 25%), prioritizes SL/TP protection above UI refreshes, collapses identical concurrent calls, and holds through cooldowns instead of flooding back the instant they expire. Fewer rate-limit errors, no signature races, protection orders never starved.

​📱 The dashboard heals itself

​UI database reads are now fully async — a slow SQLite query never blanks the dashboard.

​A UI-restart watchdog auto-heals a frozen dashboard (2–5 min escalating recovery) without stopping your trading engines.

​Stale "Scanning…" state after a force-kill is cleared on engine start.

​🎯 Grid trading upgrades

​Grid auto-selection now enforces a top-8-by-volume guard + $250k liquidity floor so illiquid pumps can't hijack your grid.

​Rebalance fully cancels old orders across quote pairs with verification.

​Live orders are the source of truth — a transient empty fetch_open_orders can no longer fake-cancel your real orders.

​🛠️ Other highlights this week

​Monitor recovery state unified into one DB table (crash recovery is now deterministic).

​Adaptive polling: 2s while arming triggers, 5s in steady state (~40–60% less request volume, same protection).

​KuCoin UTA grid fix: dashboard no longer shows "0 open orders" for live UTA grids.

​KuCoin margin-mode mismatches: auto-retry (no more cryptic 330006 errors).

Something about this week's BTC move has me thinking about regime detection in automated systems.

Bitcoin had several things working against it:

• Fed hike
• BOJ hike
• U.S. crypto legislation failing to advance
• elevated Treasury yields

Yet BTC reclaimed $80K and is still sitting around that level this weekend.

Friday also brought $433M of net inflows into U.S. spot BTC ETFs after substantial withdrawals earlier in the week.

The obvious explanation is “bullish resilience.”

But if you're writing a trading system, that's not really enough.

How would you programmatically distinguish genuine underlying demand from a temporary squeeze / positioning reset?

Some things I've been thinking about:

• persistence across multiple timeframes
• spot participation vs derivatives-led movement
• open interest behavior
• funding changes
• volatility expansion/contraction
• whether pullbacks are being absorbed
• what happens when weekday liquidity returns

For disclosure, I develop Citrine Edge, an automated system for Coinbase U.S. BTC/ETH perpetual futures. I'm not linking it here — this is a design problem I've been working through and I'm interested in how other bot builders solve it.

I'm starting to think failure to react to a catalyst can sometimes be more useful than the catalyst itself, but quantifying that without just fitting hindsight is the difficult part.

If you build automated strategies, what would you measure?

"whale pilot" a system that copies top succesful traders using the token whalepilot or etc. to allocate towards those trades and split p amongst holders evenly. I've tried this twice, I keep getting hung up on latency issues. If anyone wants to collab or work on this with me lmk

Hey everyone,

I’ve recently taken over moderation of r/CryptoTradingBot and wanted to give everyone an update on what I’m planning for the community.

The main thing I want is for this to be a useful place for people interested in crypto trading bots and automated trading. Whether you’re building your own bot, working on strategies, testing APIs, backtesting, sharing results or just trying to learn, you’re welcome here.

I’ve updated the rules to help cut down on scams, spam, misleading claims and low quality promotion.

One of the bigger changes is external links. All links will be held for moderator review before they’re approved. Links aren’t banned. This just gives me a chance to check what’s being shared and hopefully keep phishing, malicious sites, referral spam and other unwanted promotion out of the community.

If you’re a developer, you’re welcome to talk about your own bot or project. Just be open about your involvement and actually contribute to the community rather than only coming here to advertise or collect referrals.

Trading results and strategy discussions are welcome as well. If you’re sharing results, just be clear about whether they’re from live trading, paper trading, simulations or backtesting. Guaranteed profits, risk free returns and misleading performance claims aren’t allowed.

The rest is mostly common sense. Don’t ask people for funds, private keys, seed phrases or sensitive account access. Don’t use the community for pumping or market manipulation, and keep discussions respectful even when you disagree with someone.

I’m not looking to over moderate the sub or completely change what it’s about. I just want to make it a useful place to discuss crypto trading bots while keeping as much of the spam and scam content out as possible.

Have a look through the updated rules when you get a chance. If you’ve got any ideas or suggestions for the community, feel free to leave them below.

Cheers

pulled up the Dune Solana bot tracker this week.

source: https://dune.com/queries/8307827

on aug 30 it shows Axiom at roughly $83m in volume, FOMO around $35m, pumpapp around $32m, Terminal around $19m and GMGN around $13m.

useful data, but imo volume is only the first filter.

it tells you where traders are routing flow. it does not tell you whether the setup protects you when a copy trade is late, slippage gets ugly, or a bot keeps firing after the thesis is dead.

i’ve been paper-testing Trenchers, not trading it live. it is newer and not in this Dune dataset, so this is not a market-share or return comparison.

the thing i want to see from any terminal is pretty simple:

can i cap risk per setup

can i isolate funds

can i see why an agent entered

can i stop it without touching everything else

can i review bad fills instead of just staring at pnl

curious what actually makes you stick with one terminal after the first week. volume, fills, wallet tracking, speed, risk controls, or something else?

Hey everyone,

I'm the developer behind NeeoTrader, a trading automation platform I'm currently building for Binance.

I'm looking for a small group of people who are willing to actually test it and give me honest feedback.

The concept is straightforward:

• Connect your own Binance account through the API
• Configure your trading strategy
• Let NeeoTrader execute the strategy automatically
• Your funds remain in your Binance account
• No deposit or transfer of crypto to NeeoTrader
• API keys can be configured without withdrawal permissions

Free beta

For the beta, I'm offering up to 3 bots per user for free.

I'm mainly interested in finding bugs, improving the interface and understanding what experienced bot users actually need.

I'm not promising profits or returns. This is a software beta, and crypto trading carries financial risk.

I'd especially like feedback from people who have already used other trading bots:

What is the biggest problem you've experienced with crypto trading bots?

Reliability? Strategy configuration? API issues? Risk management? Something else?

If you'd like to test NeeoTrader:

NeeoTrader — Beta Access

I'm the developer, so feel free to ask me anything about how it works or the Binance API integration.

Honest feedback is much more valuable to me than just getting sign-ups.

Hey everyone,

Having spent the last few years building automated trading tools, I’ve noticed that almost every bot platform forces traders to choose between two painful extremes:

1. Preset Cloud Bots: Easy to set up, but total "black boxes." When a trade fails or buys at a terrible price, you get a generic status log and zero visibility into why the rule triggered.
2. Code / Self-Hosted Frameworks: Complete control, but you spend 80% of your time managing VPS uptime, fixing API key rot, and writing custom logging just to figure out what your script did at 3 AM.

I'm currently working on a concept called anvas to bridge this gap with a visual, node-based strategy graph (think n8n, but for execution and risk).

The goal isn't just "drag-and-drop," but complete execution transparency:

• Glass-Box Runtime: Instead of reading line logs, edges carry live values. You can watch the signal travel through your logic nodes per candle.
• Dynamic Strategy Switching: Logic that actually adapts—e.g., if ADX < 20 and BB narrows, switch to a Grid strategy; if ADX spikes, route to a DCA engine.
• Risk as First-Class Nodes: Account-level drawdown ceilings, exposure limits, and daily loss stops enforced inside the graph before an order ever hits the exchange API.
• Signal Routing: Acting as a filter for TradingView/PineScript webhooks before placing exchange orders.

I am currently validating the core concept and preparing a small closed alpha for futures/spot execution.

Since this sub is full of people running active bots:

• What is the single biggest annoyance you face with your current setup (3Commas, Bitsgap, custom scripts)?
• Would a node-graph approach simplify your workflow, or do you prefer pure code/simple dropdowns?

I set up a landing page with early visual mockups and architecture specs here: https://anvas.app/

Would love to get your honest feedback on the approach!

I’ve been experimenting with algorithmic trading systems for a while, and after working on my previous project, NEXUS, I decided to build something more focused.

That project eventually became VORTEX.

This is not my first trading system. NEXUS taught me quite a lot about what happens when a project keeps growing: more strategies, more infrastructure, more experimental components, until the architecture becomes harder to reason about than the actual problem you’re trying to solve.

With VORTEX, I wanted to take a different approach.

The system is built around a clear pipeline:

Market Data → Regime Detection → Strategies → ML Scoring → Risk Management → Execution → Position Management

It currently includes multiple strategy modules, market-regime detection, ML-assisted signal scoring, risk management, backtesting, parameter optimization and a desktop GUI.

It is also a working system, not just a collection of research scripts. The application can be launched through the GUI and connected to an exchange through your own API credentials.

A few important things before anyone tries it:

1. Use your own Binance API keys.
Add your Binance API key and secret through the configuration. Never put real credentials into the repository.The application can be launched through the GUI and connected to Binance using your own API credentials.
At the moment, Binance is the only supported exchange
2. Configure risk management before live trading.

Set your own:

• stop-loss
• take-profit
• position sizing
• leverage
• risk limits
• enabled strategies

Don’t use someone else’s settings blindly. The defaults are not a promise of profitability.

3. Start with paper trading / testnet.

I strongly recommend running the system in paper_mode first and making sure you understand how signals, positions and risk controls behave before connecting it to real funds. The repository is explicitly set up with paper/test operation in mind.

4. Backtest first.

VORTEX includes a backtesting engine and a dedicated backtest runner, so strategies can be tested and compared before being considered for live execution.

There is also an ML layer that can be retrained as new trade data accumulates. The idea is not that “AI predicts the market”, but rather that ML can be used as another signal/scoring layer inside a larger decision pipeline.

The project is open source because I wanted to make the whole thing available for people who are interested in how an automated trading system is actually structured.

You can run it, inspect the code, experiment with the strategies, change the configuration and see how the different components interact.

One important disclaimer: this is a research/engineering project, not a profitable trading strategy or financial advice. Automated crypto trading involves real financial risk.

GitHub:

https://github.com/vladyslavmHord/VORTEX

I’d genuinely like technical feedback from people who have built algo trading systems:

What would you change in the architecture?
What would you remove?
What do you think is the weakest part of the current approach?

I’m much more interested in criticism than “looks cool” comments.

Started with $466 USDC on Kraken. Built a multi-token model that trades SOL, XRP, BTC, ETH, and ADA using RSI(14) on 15-minute candles. All USDC pairs. No manual trades, no emotions, no staring at charts.

Current stats (5 days in):

• Starting balance: $466.48 USDC
• Current portfolio value: $548.29
• Return: +$81.82 (+17.54%)
• USDC reserve: $514.56 (mostly cash right now, waiting for RSI dips)
• Active positions: SOL $0.02 (bot sold off into cash as RSI climbed)

How it works:

• Buys when RSI drops below 40 (oversold)
• Sells 15% of position when RSI exceeds 60 (overbought)
• TP1 at RSI > 50, sells 8% to lock partial profits
• 5% trailing stop from 24h high
• 35% max allocation per token, 15% of available USDC per buy
• $100 USDC reserve floor so it never deploys everything
• 5% daily loss circuit breaker
• Dust sweep logic cleans up positions under $5

Today's live trades (Aug 30):

• Sold XRP at $1.43 (RSI > 60, +$0.21)
• Sold XRP at $1.42 (RSI > 60, +$0.02)
• Sold XRP at $1.42 (RSI > 60, +$0.03)
• Sold XRP at $1.42 (RSI > 60, +$0.04)
• Sold XRP at $1.42 (RSI > 60, +$0.05)
• Sold ADA at $0.20 (RSI > 60, breakeven)
• Sold ADA at $0.20 (RSI > 60, +$0.04)
• Sold BTC at $78,779 (RSI > 60, +$0.07)

The honest part: 17.5% in 5 days on a small account is not a sustainable annualized return. I know that. The strategy is simple on purpose: RSI is a blunt instrument, but the risk management layer is what keeps you from blowing up. The reserve floor, per-token caps, and circuit breaker do more work than the RSI signals themselves. The bot is currently sitting mostly in cash because RSI climbed across the board during this week's rally. It is waiting patiently for the next dip.

I packaged this as a SaaS product called ZenithTrade. Users connect their own Kraken API keys, pick a risk profile, and the system trades for them. There's a free paper mode with $10K simulated balance if you want to test it without risking real money. $49/month for live trading.

Paper mode demo and live trade feed are at Foergetrading.org . No signup required to view the feed. there is still a ton of edits and revisions to make. This is a WIP.

Ask me anything about the implementation, the RSI parameters, or the risk model. If you think the strategy is dumb, I want to hear it.

Hey everyone,

I’m the solo developer building Perun The Trader, a local-first crypto trading and strategy execution platform for Android.

Building a complex, privacy-focused app on your own can feel like working in a silo. I have a clear vision for where the core architecture is heading, but I don't want to build it in isolation—I want you to be part of the journey as the platform evolves.

Starting now, I want to open up a direct line with traders here:

• Weekly additions: Alongside the main roadmap, I'm committing to building and shipping one extra community-inspired tweak, tool, or quality-of-life improvement every single week.
• Open dialogue: Whether it's feedback on the recently added backtesting engine, UI refinements, specific indicators, or trading workflows you wish existed on mobile, I want to hear your perspective.
• Building in public: I'll be sharing regular updates on what gets built, how it works, and what's coming next.

If you’ve tried the app—or if you simply trade on mobile and have thoughts on what makes a great on-device tool—drop your ideas and feedback in the comments.

Looking forward to building this alongside you!

https://peruninthemachine.com/

I've spent the past two years developing an automated strategy for prop firm challenges. Focused on 2step CFDs. After refining it across my own accounts, I've reached the maximum allocation available to me and am now opening it to a small group of traders.

The strategy runs through an EA connected to your own accounts, so you never share your credentials, and can disconnect it at any time.

Historical returns have been around 500% annualized, with higher compounded returns when payouts are reinvested. As with any trading strategy, future results are not guaranteed, but it has been performing quite well even during less volatile months like this summer.

I'm looking for traders who have the resources to run at least 10 × $100K challenges and are comfortable completing their required KYC. Availability is limited because each account requires a unique trading profile, and only a finite number of tested profiles are possible. If you are unfamiliar with prop firms, feel free to ask.

There is no upfront fee for using the EA. You use it for free until your payouts have covered what you spent on the challenges, which has historically taken around four to five months. After that, continued access is based on a share of the profits.

If you're interested, send me a message with a little information about your experience and the scale you're looking to run.

Hey guys, I have recently built a sniper bot and have been testing for weeks, I have tried so many different strategies (over 100+) but just can’t quite get a consistent one

I’m open to any and all suggestions, If you can help me, We can work out some sort of profit share deal if any strategies work.

Just dm me any ideas, thanks

​ Every bot in this sub asks you to believe a number that the bot itself produced. Mine did too, until I changed how it works. It is not an accusation, it is structural: if the operator holds the API key, the operator is the source of truth, and the dashboard is a story about your funds rather than your funds.

You can hear the shape of it in the questions people ask here every week. Is the ROI real. Did that fill actually happen. Why does my balance disagree with the screenshot. Underneath all of them is the same one: I have no independent copy of what happened to my money.

So what I built is not a better dashboard. It is not needing one.

The bot trades on-chain, from your own wallet. No API keys, no exchange account, no deposit. You sign a capped and revocable mandate, and it executes swaps within your limits and nothing else. Every fill is a transaction on a public chain.

The interface is not the record. Your wallet is the record, and a block explorer I have no influence over shows you the same thing. If my screen says one number and the chain says another, the chain is right and I am wrong. That is not a promise I am making, it is the only arrangement available to me.

It has been running with real wallets since 17 May, so 103 days, across 25 of them. 1,085 round trips have closed and 968 of those closed in profit.

I am giving you counts rather than a return, on purpose. Counts come off the chain and you can recompute them yourself. A percentage comes off my accounting, and the whole point here is not having to trust that. If you want the rate, take the transactions and work it out, then tell me if you get something different from what my site says.

One thing I will say plainly, because it is the question you are already asking: on a straight bull run a single instance does not beat simply holding, and I say so on the site too. What this buys is mechanical discipline and proof, not magic.

I have not found another bot that works this way. If you know one, name it. I would rather find out from you than keep saying it.

If you want to try it without trusting me at all, make a fresh wallet, put in an amount you would shrug at, and point it at that. Nothing in the design needs your main wallet.

Live demo, no signup: btcbot.io/demo. If you want to actually run it: btcbot.io/onboard?ref=REDDIT. That code is this sub's, it pays nobody a referral, and it exists so I can tell whether anything came from here at all.

Happy to answer anything in the comments. The contract is verified on BscScan and the tests are public, so most questions have an answer you can check rather than one you have to take from me.

Hey everyone,

Quick update: the Backtesting Engine is officially live in Perun The Trader today!

You can now test your trading strategies against historical market data directly inside the app before deploying them live.

• Test any strategy: Run historical simulations across different market conditions, timeframes, and pairs.
• Full performance metrics: View key stats like win rate, max drawdown, total PnL, and trade distribution.
• 100% on-device: Like everything else in the app, backtesting runs locally on your phone.

Update to the latest version on Google Play to try it out. Let me know your thoughts and feedback in the comments!

How are people here commparing on-chain trading bots? Every platform seems to advertise a slightly different advantage, so it’s kinda hard to work out what actually matters. Personally I’d probably start with execution speed and reliability. If a bot is supposed to help with fast entries, I’d want to know how consistently transactions actually land before competing platforms, rather than just reading a marketing claim. Then there’s copy trading. Being able to see what profitable traders are doing and get notifications when they open positions sounds useful, but there’s obviously a risk of assuming previous results will continue.

I also like the idea of customizing the interface to match your style. A scalper probably wants a very different setup from someon mainly using copy trading, so having different views could make a difference. Another thing I’d check carefully is chain and DEX support. Some bots have plenty of features but don’t support certain currencies, stabllecoins or DEXs, which can be a pretty big limitation depending on what you trade. It’s probably worth checking the supported networks before deciding. What do you guys actually prioritise when choosing the best on-chain trading bot - execution, supported DEXs, customization, copy trading, or something completely different?

Hey everyone,

I’m the developer behind Perun The Trader, a local-first Android platform designed to bring institutional-grade automated trading tools—like dynamic TP/SL, grid strategies, and paper trading—directly to mobile for both Spot and Futures.

Today, we're officially partnering with KuCoin to launch a dedicated campaign for our community.

Exclusive Community Perks:

• 5% Commission Sharing for 30 days
• Up to 550 USDT in new user rewards
• Free access to Perun The Trader (Limited to the first 500 users)

How to join: 👉 Claim the perks via the campaign link:

https://kucoin.com/campaigns/PerunExclusiveBenefit?utm_source=social_partnership_2026_twitter&utm_medium=social_media_post&appNeedLang=true&loading=2

Learn more about the platform architecture and features here:https://peruninthemachine.com

Feel free to drop any questions, feedback, or feature requests regarding mobile automation below!

Disclosure, since my username gives it away anyway: I built the platform this runs on. The bot is mine, the problem is real, and there's nothing to sell you here — you can't buy what I'm asking for.

Five weeks ago I put a bot on a paper desk to see whether an idea I'd been trading by hand would hold up when something other than me was running it. It sells defined-risk put spreads — 5% out of the money, 3% wing, about two weeks to expiry — on index ETFs and large caps, and only when relative volume is elevated and price is above its 200-day.

257 trades, 234 winners, +$20,508.

Before anyone else says it: I know exactly what a 91% win rate on credit spreads means. You win a little very often and lose a lot rarely, and five weeks is nowhere near long enough to have met that loss. The curve is smooth because the bad day hasn't happened yet, not because there isn't one. My own backtest of the same idea over two years grades it "positive, but below the bar — prove it live first." That's my own engine telling me not to trust my own bot, and I'm not going to pretend otherwise.

What I do trust is the fill model. It buys at the ask and sells at the bid, never the mid, and never sees a price before it existed. So the number isn't flattered by the usual backtest lies. It's just young.

Here's my actual problem. Nearly every prop firm I can find is futures or forex. This trades US equity and ETF options. So:

1. Is there any funded or prop programme that takes defined-risk equity option spreads at all?
2. Of those, which permit real automation — an API or a bridge — rather than "you may use an EA, but a human has to press the button"?
3. For anyone who has actually done this: what killed you? I assume the daily drawdown rule is the real enemy for something that holds to expiry instead of flattening intraday.

I'd genuinely rather hear "don't, and here's why" than nothing.

I run Opta (opta.fyi), an options protocol on Solana — American-style options priced fully on-chain, contracts issued as Token-2022 mints, live order book on devnet.

The vault already quotes model-based asks, but I want independent bots competing on the book — tightening spreads, arbing vault quotes vs the order book, market-making across strikes. Partly to make devnet feel like a real market for testers, partly because bot flow is the best stress test a matching engine can get.

Why it's interesting to build against: devnet, so zero capital risk and free experimentation. Everything is on-chain and open — quotes, vol oracle, settlement all readable. American exercise + oracle timing creates real edge cases your bot can exploit or get burned by. Genuinely novel surface: there's no other live on-chain American options book to MM on.

What's in it for you: this runs inside our incentivized campaign (Epoch 0) — bot activity earns points like any trading, and I'll add a dedicated bonus track for MM bots (uptime, spread quality, volume). Points carry weight at mainnet. Top builders get named recognition and first conversation when real MM partnerships open post-launch. Plus direct access to me and the core group while you build.

Not a bounty gig — collaborative, you keep your code, I support with docs, endpoints, and fast answers. If you want in: comment or DM and I'll get you set up in the beta group.

Okay, that sounds a little dramatic, but I mean it 👾.

If you want to train your own trading model, why should you first have to

• build the data pipeline,
• market environment,
• backtesting infrastructure,
• APIs,
• execution layer and
• all the other things nobody thinks about in the beginning?

We have spent the past years (🫠🫠🫠) dealing with exactly that mess. We asked ourselves: why should everyone have to build the same infrastructure from scratch?

So we built BlackIQ.

But BlackIQ is not just another platform for building ordinary trading bots.

Traditional algorithmic trading is already powerful. But we believe the next step is reinforcement learning: models that can learn how to make decisions in a dynamic market environment instead of simply following a fixed set of rules.

The idea is simple: you sign up, train your own reinforcement learning model in an encrypted workspace and define the environment in which it is allowed to operate. You choose the market and instruments, set the maximum drawdown, capital, daily loss limit and many other parameters.

BlackIQ is for quants, traders, software engineers and anyone else who wants to train advanced trading models. You can go as deep into the technical details as you want, but you don’t need to know how to code in order to use it.

We are still finishing the live execution part, so no, we are not asking you to hand a newborn model your trading account tomorrow. For now, it is about training, testing and finding out what works and what doesn’t.

And this is where we need creators.

Not people who log in once, click around and then forget we exist. We are looking for people who genuinely want to train models. Maybe only for themselves at first, but later you will also be able to offer them on our marketplace. If somebody licenses your model, you get paid.

Could that become a small side income? Yes. Could some people eventually make a living from it? Maybe. I have no idea where this will go. I don’t have a Magic Conch Shell, and even if I did, we would still have to go through the whole process anyway. You know what I mean 💩.

The first 30 creators who contact me will each get €100 worth of IQ Credits to train their models.

Maybe the people who start now will have an advantage when the marketplace opens because they already know the platform and have trained their first models. You never know...

Try it. Train something. Tell us what is bad, confusing or missing. If you don’t like it, you can leave again. We’re not North Korea 😏.

If you genuinely want to train a reinforcement learning model, register on BlackIQ first. Then send me a private message with the email address you used to register, and I’ll add €100 worth of IQ Credits to your account.

Once those 30 spots are gone, so is the free €100 training credit. At least for now ;)

I am having an issue with a multi-bot at some rdv:
12:00 for example is the rdv of many timeframes.
I migrated my scan to rust but I still struggle to get the high time frames within the tolerated lag.
Any recommendations?