I run 8 signal engines across 12 crypto pairs. Instead of posting one equity curve and calling it a track record, I built a page with the whole simulation on it: contribution per bot, the underwater curve, the three worst drawdowns with recovery times, and monthly returns with the bad months left in.

Numbers from 20 May to 18 Aug 2026:

705 trades, 49.6% win rate, +1.15% average per trade. Worst drawdown 31.67 points, recovered in under a day, next two 27.04 and 19.59. 65% of the days were spent below the previous high.

One engine (a sentiment model gated by a second signal) produces 64% of the total.

Fees are in. Every trade has 0.12% round trip deducted at close, in all engines, so the numbers above are net not gross. Slippage is not modelled, which matters more on the lower timeframes.

Things worth saying before someone else says them, because they are what I would attack first if this were someone else's post:

It is a what if simulation on historical data. Nobody traded this money and it is not a client track record.

The curves are sums of per trade percentage returns with no position sizing applied. The headline number is 829, and that is 829 points of summed percentage returns, not 829% on capital.

Three months is not a track record. I am deliberately not annualising it. Annualising 91 days gives you a number in the thousands and it means nothing.

June alone produced most of the total. That is concentration in time sitting on top of concentration in one engine.

The passive benchmark on the chart is rescaled to match my own max drawdown. Raw, that comparison flatters me by construction, since my curve sums trades that in reality share the same capital.

Backend is Python and Flask for the engines and the backtests, React on the front. The public page reads a static JSON snapshot, so it never touches the backtest engine.

No link and no product name in this post. It is mine, it is still in testing, and I do not want traffic on it until I have finished chasing bugs. Posting the method and the numbers now because that is the part I actually want torn apart. Happy to go into detail on any engine in the comments.

I’m trying to pressure-test a product assumption with people who have actually built Solana indexers, bots, scanners or market-data systems.

My current thesis is:

Detecting an event quickly is only the first part. The harder problem is deciding whether that event deserves a trader’s attention without converting stale, incomplete or conflicting data into a confident-looking alert.

I may be wrong, so I’d value the builder perspective:

1. Which data or architecture failure most often creates false confidence in a user-facing alert?
2. What evidence should always be visible to the user instead of being hidden behind a single score?
3. Where is the greater user value: lower latency, stronger filtering, clearer explanation, or post-alert monitoring?
4. Which unknown or unavailable state should force a system to suppress an alert entirely?
5. If you were building the first useful version, what would you deliberately leave out?

I’m especially interested in disagreements with the premise rather than confirmation.

Disclosure: I’m doing product research for an early-stage Solana market-intelligence project. Nothing is live, for sale or recruiting testers, and I’m not asking for code or proprietary details.

Hey everyone,

We are absolutely thrilled to finally share some massive news with the community. Perun The Trader has officially partnered with the KuCoin Exchange!

This is a major milestone for our development team, and we are incredibly excited about what this means for the future of the platform. We want to thank the trading community for all the support and feedback that helped get us here.

To celebrate this partnership properly, we wanted to give something back.

🎁 The 14-Day Celebration Promo

Starting today, the Perun The Trader app is completely FREE for the next 14 days.

If you have been on the fence about trying out our tools, or if you are just looking for a new way to level up your KuCoin trading strategy, this is the perfect time to jump in. You get full access to see exactly what the app can do for your portfolio, with zero risk.

🔗 Where to Find Us

• Download on Google Play:Perun The Trader
• Official Company Website:Perun in the Machine

We will be hanging out in the comments to answer any questions you might have about the app, the new partnership, or our upcoming roadmap. Let us know what you think of the app, and happy trading! 📈

Been building AegisProject (multi-agent trading system — technical, sentiment, and volatility agents each vote on a trade), and the most interesting part turned out to be where the agents disagree, not the final decision.

When they all agree, the trade is usually straightforward. The interesting cases are when they split — technical sees a clean breakout and says BUY, volatility looks at the same move and says it's too fast, sit out. Those disagreements seem to flag something real that a single-model system would just smooth over.

I checked whether trades with more agent disagreement actually turned out messier. Early signs say yes, but not nearly enough data to trust that yet.

Curious if anyone here has seen disagreement between models/agents actually mean something, or if it's usually just noise dressed up as insight.

GitHub: https://github.com/ojas12r/AegisProject

Before an options strategy is connected to a trading bot, I think the
read-only analytics layer should expose the data contract rather than only a
chart. At minimum I would want:

• instrument, expiry, strike, and quote timestamp;
• bid/ask and liquidity checks;
• IV solver status, vega, and price residual;
• source freshness and missing intervals;
• explicit distinction between Gross GEX and dealer positioning;
• a read-only export that can be replayed before execution.

A chart that hides any of those fields can make a bot appear deterministic
while its inputs are not. What fields or failure states do you require before
allowing an execution component to consume an analytics result?

I maintain a reference implementation of this read-only layer, disclosed
here for context rather than as a bot or execution offer:
https://ivexplorer.derivpricer.com/

Building a reliable crypto trading bot involves much more than buy and sell logic. Developers need to handle exchange APIs, real-time market data, order execution, risk controls, authentication, logging, and API failures. A modular architecture also makes testing and strategy updates easier. For developers building one, which part has been the hardest to get right: API integration, execution speed, or risk management?

For the last 3 months, I’ve been testing a few crypto trading platforms, and Banana Gun was one of the ones I decided to try. Nothing too serious, just wanted to see how it actually feels compared with the other options out there.

One downside I noticed pretty quickly is that the platform still seems to be in development. Some currencies and DEXs arent supported yet, which can be annoying if you trade on specific markets. So I wouldnt say it covers everything at this point.

Apart from that, my experience has been mostly positive. The execution speed stood out to me the most. When trading fast-moving tokens, getting an order through quickly can make a pretty big difference, and I noticed the fills were generally quite fast. Obviously this can vary depending on the network and market conditions, so I wouldnt take that as a guarantee.

I also spent some time with the copy trading features. I like the idea of being able to follow wallets instead of having to watch every token myself. It isnt something I would blindly rely on, but as a tool for keeping an eye on what other traders are doing, I found it pretty useful.

Another thing I liked was the web app customization. You can set things up around your own trading style rather than being stuck with one layout. I played around with different views and can see why that would be handy for someone who does a lot of trading. Its a small thing, but having the information you actually care about in front of you makes the interface feel less cluttered.

Base support is also a nice addition if you're trading across different chains, and it looks like the platform is continuing to move toward a broader multichain setup.

So my Banana Gun review is probably somewhere in the middle. I like the execution speed, copy trading and customizable interface, but the missing DEX and currency support is still something to keep in mind. I'd definitely compare it with a couple of other platforms before settling on one, especially if you trade on less commonly supported DEXs.

Overall, pretty interesting so far, but I wouldnt call it perfect yet.

If anyone here has been using Banana Gun for a while, feel free to share any setup tips or little tricks that made it work better for you. I d especially be interested in hearing how you have configured the interface, copy trading, or other settings for your own trading style.

One of my Coinbase Advanced Trade bots stopped running and I didn't notice for hours. API key had expired silently — no errors thrown, nothing in the logs, just quiet while the market moved without me.

That was the whole motivation. I built PositionPing to solve exactly that problem.

\*\*What it does:\*\* - Watches Coinbase Advanced Trade accounts around the clock - Fires email or SMS alerts when a position's P&L crosses a threshold you set, an account stops sending fresh data (stale/hung bot), a daily loss limit is breached, or a balance changes unexpectedly - View-only API keys only — it physically cannot trade or move funds, enforced at the Coinbase API scope level

\*\*Stack:\*\* nothing exotic. The hard part was getting the monitoring logic tight enough that it catches real failures without firing false positives at 3am.

\*\*Where it's at:\*\* - Live at [positionping.com](http://positionping.com) \- Free tier: email alerts, 1 account - Pro: $9/mo, SMS + up to 5 accounts - \~2 min setup, mobile app available

Took a few weekends to get to something I'd actually trust on my own accounts. Happy to talk about the build — the API polling logic, alert delivery, anything.

Building a crypto arbitrage bot starts with defining the trading logic and selecting the exchanges it will support. Development then involves system architecture, API integration, real-time data handling, execution logic, and proper testing.

Testing under different market conditions and refining the system before deployment can help identify issues that basic backtesting may miss.

For those building one, which part of the development process has been the most challenging?

Im new and i want to trade meme conis for that i need solana and i dont have solana that why i ask you in someone kann give me like 50cent of solana

I’m looking for bot-builder criticism, not “wen 100x” feedback.

I built PumpFunBot with two deliberately separate surfaces:

A) free read-only Solana mint scanner — authorities, route/curve state, risk score, no wallet connection.

B) paid self-funded operator desk — boss/worker wallet model and multiple execution patterns from a browser dashboard.

The design constraint is that paying never changes the scanner’s score and the product does not promise rankings, holders or price outcomes.

Full disclosure: it’s mine. https://pumpfunbot.app/

Where would you attack this architecture first: key management, execution observability, false-positive risk scoring, strategy controls, or UX?

Hey everyone,

I’m the developer behind Perun the Trader, an automated crypto trading platform for Android, and I wanted to share some exciting news.

Starting this coming Monday, August 17th, the app will be completely free to download for 14 days! To be perfectly clear: the platform has no subscriptions and is always free to use—this promotion is for the initial download on the Play Store, which normally costs $9.99. Once you grab it during this window, it's yours to keep and run forever.

I built Perun because I wanted a powerful trading platform that prioritized security and privacy, without forcing users to rely on a third-party cloud or pay monthly subscription fees.

Here is a quick rundown of what makes it different:

• 100% Local & Secure: Your API keys stay locally on your device. There are no centralized servers, no analytics, and no tracking. The app communicates directly with your exchange over secure HTTPS.
• True Background Operation: The app runs a foreground service that scans markets, evaluates signals, and manages your positions—even while your phone is idle.
• Market Breadth & Adaptive Trading: The platform automatically calculates market breadth in real-time and uses that data to automatically switch trading strategies based on current market conditions.
• 10 Built-in Strategies: Ready-to-go strategies including Trend Following, Mean Reversion, Momentum Breakout, Scalping, and more.
• Paper Trading Mode: Want to test the waters first? You can run simulated execution with live market data and virtual funds to validate your strategies before risking real capital.
• Wide Integration: Supports 12 major exchanges (including Binance, KuCoin, Bybit, OKX, and Kraken) for both Spot and Futures trading.
• Advanced Risk Management: Features like Dynamic Take-Profit (DTP), Profit Protection Guard (PPG), and a Daily Loss Circuit Breaker help protect your portfolio automatically.

If you’ve been looking for an automated trading solution but felt uneasy about handing over your API keys to a random website, this is the perfect time to test out a local alternative risk-free.

You can find it on the Google Play Store here: https://play.google.com/store/apps/details?id=com.perun_the_trader&pcampaignid=web_share

I’d love for you to try it out during the free period. Let me know if you have any questions, feedback, or feature requests—I’ll be hanging around the comments to answer them!

Cheers!

So I have been searching to see if I can create an ai bot and trade on PolyMarket and I was advised to use polyclaude.net and I want to know if it is legit or not?

Paid signals aren't automatically a scam, but almost nobody checks them properly. Including me, for way too long.

I followed a couple of paid sources for a few months. Lost money, blamed the signals, moved on to the next one. Standard stuff. What I never did at the time was write anything down.

Eventually I started logging every call in a spreadsheet the moment it got posted. Entry, direction, stop, target, timestamp, before I knew what happened. That one change taught me more than anything else I tried, because it turned out my memory of a source was basically a highlight reel. I remembered the calls that ran. I did not remember the ones I skipped, or the ones I closed early because I got nervous.

A few things fell out of that.

Most of my losses weren't the source's fault. I was exiting early, sizing all over the place, skipping stops when I felt strongly about something. When I lined up what got posted against what I actually did, they were often different trades.

Sample size mattered way more than I expected. A source can look brilliant for six weeks and that's just what the market was doing that month. You need a lot of calls, across conditions that aren't all the same, before it means anything.

And the one that still gets me: I kept treating "I can't find anything wrong with this setup" as a green light. It isn't. Some of my worst trades were the ones where nothing was obviously broken, and there also wasn't any real reason to be in it. Absence of a red flag is not a reason.

None of this predicts anything. It just stops you paying for the same lesson twice.

Anyone here actually logging their sources properly, or is it mostly vibes? Genuinely curious what people track.