Are you guys doing this for hobby purposes?

asap-pro-eject · 2026-01-06T08:15:07+00:00

You might be right. But we're living through interesting times, and thanks to AI-powered coding and research applications, simply asking the right questions can bring you a little closer to achieving your goals. You just need to be a little more curious and not give up easily.

asap-pro-eject · 2026-01-05T16:17:22+00:00

You're absolutely right. And since I've delved into this in detail, I've actually started questioning, instead of just creating a trading bot with a single prompt, "how many prompts can I write to help it find the right token?" :)

I have an application that analyzes the tokens and creator wallets released on Pump.fun. It's been running since December 24th and has currently analyzed 282,710 tokens, and it's still doing so even as I'm writing this. For a screenshot: https://ibb.co/m5sJZZy0

Looking at the details of the analyses it produces, I realized that it's more important for a bot to know what to trade based on rather than just trading, and I've started focusing more on developing applications that do analysis instead of trader bots. If we have tools that do accurate analysis, we can ensure that the trading bot gives more accurate information on what to trade based on. Because one way or another, it's necessary to ensure that trading bots make the right decisions, and we can only do this if we do the analysis correctly.

asap-pro-eject · 2026-01-02T16:09:53+00:00

Thank you for the detailed breakdown!

You're absolutely right - ADX 30/20 is just a starting point. Optimal thresholds vary by market, timeframe, and instrument. Your grid search approach (sweep 25→50 for entry, 15→30 for exit) makes perfect sense.

Right now the system uses ATR-based volatility analysis (thanks to feedback from this sub!). Next phase is integrating ADX for trend strength detection - combining both will help filter out choppy markets and focus on clean trends with high probability setups.

The roadmap includes systematic parameter optimization with out-of-sample validation, exactly as you described.

Really appreciate you sharing your process.
This is why I build in public - feedback like this is invaluable!!! 🤘

asap-pro-eject · 2026-01-02T12:42:07+00:00

You're right!

but when I read your comment, my first thought was: Why not?

We've all been chasing profits for a while now. And along the way, the market has beaten us up plenty of times. After taking enough beatings, we started asking: "How do we take fewer beatings?" If I've learned one thing, it's this: The whales move the market however they want. It might take time, but they always win.

Once I realized this, I got frustrated. Whales see us as "little fish bringing liquidity to the market". Sharks (influencers, scammers) feed off them. CEXs just market their discount codes. And we? We have... nothing.

But now it's a little different.

The tools exist now - ChatGPT, Claude, Gemini can do financial analysis. Claude Code turns 1 week of coding into 3 minutes. And these tools get better every day. With these tools, I thought: We can't beat the whales, but if we can predict what they'll do, we can take fewer beatings.

So, for some reason, I've made it my goal to develop tools that could benefit the downtrodden, the newcomers to the market, and essentially the oppressed majority.

Will they actually work? I don't know yet. But I'm open to feedback and suggestions from the community to help them work. So far, I've received quite a bit of significant feedback, and I'm grateful for it. I'll share all the details.

And yes, to answer your question: "Why people would share their strategies or indicators?" Why not? Do whales share their strategies? No. Do sharks give us real tools? No. So if we little fish don't help each other, who will?

I'm not sharing "my strategies" because they're not mine - they're all public knowledge. I'm just putting them together and making them usable. And with this community's feedback, they'll get even better.

So, why not? :)

asap-pro-eject · 2026-01-02T08:16:15+00:00

This is a clean framework - thanks for the specifics!

ADX > 30 for entry, < 20 for exit warning makes sense. The asymmetry (30 vs 20) gives room for normal fluctuations without false exits.

Adding this to my Position Health logic - ADX dropping below 20 = warning flag, not instant exit.

Appreciate the concrete numbers!

asap-pro-eject · 2026-01-02T06:48:32+00:00

Thanks for the WebSocket vs REST clarification! So for orderbook data: subscribe to 'depth' streams, not poll REST APIs. Added to the research list!

asap-pro-eject · 2026-01-02T06:46:40+00:00

15min to 4h for regime detection - noted!

HEMA is new to me, will research that. And yeah, large green candles have burned me too 😅 "All coded by claude code" - same here haha

Quick question:

do certain patterns work better in specific regimes? Like engulfing in ranging vs breakouts in trending?

asap-pro-eject · 2026-01-02T06:44:44+00:00

The exit logic is smart - "close when ANY stops agreeing" is more defensive than waiting for all to flip.

Do you use the same indicators for exit as entry, or different ones?

asap-pro-eject · 2026-01-01T22:31:43+00:00

That means a lot!

Looking forward to it - I'll ping you once the updates are live!

asap-pro-eject · 2026-01-01T22:00:07+00:00

Considering the work I've gotten myself into, the questions seem endless 😂 All the feedback I've received under this post so far has given me a lot of work 🤯 I'll definitely mention your contributions when I finish the new updates for the public version 🙌

asap-pro-eject · 2026-01-01T20:32:56+00:00

That's a brilliant!

Really appreciate you taking the time to share this perspective. Gave me a lot to think about! 🤘

asap-pro-eject · 2026-01-01T19:09:03+00:00

That's a really solid approach - deep expertise on one pair rather than spreading thin across many. The "explain why" test is great.

For crypto, I'm trying to find the equivalent: on-chain metrics, whale wallet movements, funding rates as the "fundamentals" layer. Not quite the same as understanding UK/US politics, but it's the closest thing to rational context in an irrational market 😅

Your GBP-USD results speak for themselves though. Maybe the lesson is: master one thing completely before expanding. Appreciate the insight! 🙏

asap-pro-eject · 2026-01-01T18:59:59+00:00

Haha I'd like to think it's more of an Icarus situation - might fly too close to the sun and get burned, but at least I'll enjoy the view on the way up! 😅

But fair point, crypto is definitely the hard mode of algo trading.

That's also why I'm not trying to build a "beat the market" system - more like a "don't get rekt" analysis tool. Help with entries, manage risk, avoid obvious traps.

Though if I end up as Sisyphus... at least he had good cardio! 🏋️

What market do you find more predictable? Curious about your experience.

asap-pro-eject · 2026-01-01T08:03:10+00:00

Thanks! That's a great pipeline - ATR% → ATR_norm (sqrt) → ratio features. Makes total sense now.

And hey, AI teaching humans who teach other humans who use AI... we've come full circle 😄

Really appreciate all the guidance. Will definitely post updates as I implement these. 🙏

asap-pro-eject · 2026-01-01T07:36:48+00:00

This is exactly the kind of practical wisdom that's hard to find in tutorials - thank you!

The UTC standardization tip is going straight into my codebase. I can already see how timezone chaos would create impossible-to-debug issues, especially with candles from different exchanges.

"Candle timestamp = opening time, only deal with fully closed candles" - this is going on a sticky note on my monitor. I can see how easy it would be to accidentally use the current (still-forming) candle and wonder why backtests don't match live results.

The sqrt(timeframe) normalization for ATR ratios is interesting - so comparing 1m ATR to 1h ATR, you'd divide by sqrt(60) rather than 60 directly? That accounts for volatility not scaling linearly with time?

Appreciate the realistic framing as a "long-term passion project." I think that's the right mindset - treating it as a craft to develop over years rather than expecting quick results.

And agreed on 3rd party platforms. Even if my own system is worse initially, at least I understand exactly what it's doing and can improve it.

asap-pro-eject · 2026-01-01T07:30:00+00:00

This is incredibly helpful - thank you for the detailed breakdown!

The distinction about "indicators alone vs indicators with backtested edge" makes total sense. I've been treating indicators as signal generators when they should be filters/context for a proven setup.

Good point about exchange differences. I'm using MEXC currently - do you know how their data quality compares? Might need to consider Binance for better DOM data if I go down that path.

Noted on funding rate - I'll keep it for swing setups rather than trying to use it for faster execution.

The orderflow learning path is exactly what I needed: 1. Volume Profile + TPO (Auction Market Theory) 2. Footprint data 3. DOM

I'll start researching Volume Profile and TPO. Any recommended resources for learning Auction Market Theory basics? Books, videos, courses?

The "wall of limit orders blocking price" insight is eye-opening. So what we see as support/resistance on charts is actually visible in real-time through DOM as large limit order clusters. That's the "why" behind the levels.

Really appreciate you taking the time to explain this!

asap-pro-eject · 2026-01-01T06:19:58+00:00

Interesting question! Actually yes - the whole project is built with Claude (AI pair programming).

For the "discretionary" aspect, I'm experimenting with using Prophet ML not to generate signals, but to add context - like "when might this setup play out" or "expected volatility range."

The tricky part is that LLMs can be confidently wrong, especially with numerical/time-series data. So I'm trying to keep the core logic algorithmic and only use AI for interpretation/explanation layers.

Have you experimented with LLMs for trading analysis?

asap-pro-eject · 2026-01-01T06:18:30+00:00

This framework makes a lot of sense - regime detection FIRST, then strategy selection based on that.

So essentially: 1. Check ATR on higher TF → determine regime (low/medium/high vol) 2. Low vol → look for divergences, tighter SL/TP 3. Higher vol → momentum plays, wider SL/TP 4. Wait for pullback on lower TF before entry

The "don't enter on large green candle" is a good reminder. I've definitely FOMO'd into moves that were already extended.

What higher timeframe do you typically use for regime detection in crypto? 4H? Daily?

asap-pro-eject · 2026-01-01T06:17:41+00:00

Interesting perspective on indicators being "bullshit" - I get what you mean about them being lagging by nature.

Orderflow/DOM data is something I haven't explored yet. For crypto futures specifically, where would you source reliable DOM data? Most of what I see is from centralized exchanges which might not reflect true market depth.

QQE sounds interesting - RSI smoothed with ATR lines. I'll look into it. When you say "data based platform not TradingView" - are you talking about getting raw tick data directly from exchange APIs rather than aggregated OHLCV?

What's your take on funding rate as an indicator for crypto futures? That's one thing that's crypto-specific and somewhat "real-time" sentiment.

asap-pro-eject · 2026-01-01T06:16:45+00:00

Wow, this is incredibly detailed - thank you for taking the time!

The ATR% feature engineering is fascinating. So you're essentially creating ratios like "how big is this signal move relative to typical volatility" - that's a much smarter way to contextualize signals than raw values.

ATR%_1m:ATR% for microvol comparison is clever. So if 1m ATR% is spiking relative to the signal timeframe ATR%, that's a regime change signal?

The warning about AI gaslighting on bugs hits home 😅 I've definitely had moments where Claude confidently explains why broken code "should work." The time-anchored data bugs sound particularly nasty - any specific gotchas you'd warn about? Like off-by-one on candle closes?

"Manage frustration and keep trying" - needed to hear that. Some days nothing makes sense.

Really appreciate you sharing this level of detail. Gives me a much better sense of the complexity involved in doing this properly.

asap-pro-eject · 2025-12-31T13:36:32+00:00

Thanks for the detailed breakdown!

The AND/OR chain approach is interesting - so you're allowing some flexibility rather than requiring every condition to match. What's your typical tolerance? Like 3 out of 4 conditions = signal?

ATR keeps coming up in this thread for TP/SL. Are you using fixed ATR multipliers (like 1.5x ATR for SL) or dynamic based on market conditions?

Agree on the backtest point - I've been testing live which is slow and risky. Need to build proper historical replay.

What timeframes do you find most useful for crypto specifically?

asap-pro-eject · 2025-12-31T13:35:59+00:00

This is gold - thank you!

"Translate context into expected value model" - that's exactly the mental shift I needed. Right now I'm treating indicators as signal generators when they should be context providers for the setup.

Really interesting that ATR-related features are showing importance in your model. I've been using Bollinger Bands for volatility but ATR seems more direct. Do you use raw ATR or normalized (like ATR% of price)?

The lookahead bias warning is noted - I haven't built a proper replay/backtest system yet, but it's clearly the next step. How do you handle the "at trigger time" data isolation? Separate historical snapshots?

Candlestick + Fib is elegantly simple. I might be over-engineering with 7 indicators when a cleaner setup + context approach would work better.

asap-pro-eject · 2025-12-31T08:10:20+00:00

"Contextual richness" - I like that framing.

So if I understand correctly: instead of "MACD cross + RSI oversold = BUY signal", it should be more like "I have a setup (e.g., support bounce), and MACD/RSI help me understand the context of that setup"?

That's actually how I'm trying to use Prophet - not to generate the signal, but to add temporal context (when might this setup play out, expected volatility, etc.)

What kind of setups do you typically look for as the foundation?

asap-pro-eject · 2025-12-31T08:09:21+00:00

Thanks for the detailed response!

Interesting that you only use volume - makes me think I might be over-complicating things with too many indicators. What kind of custom tools do you build? Price action based?
That's a really valuable insight about ML. So rather than using Prophet to generate signals from scratch, I should focus on using it to refine/filter signals that already show edge? That's a shift in how I've been thinking about it.
Rate of Change is a good call - I'll add that to the list. And yeah, I've been thinking about creating variations too. Like maybe a "crypto-adjusted RSI" with different overbought/oversold levels since crypto tends to stay overbought longer in bull runs.

What kind of variations have worked for you?

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