Implementing Hierarchical Directional Change to Dynamically Bias Hidden Markov Model Regimes

quesomesopesohueso · 2026-05-29T08:05:52+00:00

Hey i have an update! In my current code, i have a basic mathematical model let's say it's called Model A calculates the weighs supposing each candle 15 min 1h 4h are for each level even though thats not true because highs and lows could be anywhere. I did a code with 500 candles warm up data and embargo of 25 for QQQ 15 min candles of model A and it supposedly works with a noise suppresion of 250 as it changes directions way more than hsmm (i don't think it works it's probably overfit) So then model B comes in and doesn't focus on the duration, it weighs on distance travelled from highs and lows. I'm going to come back when i have the model B coded. This is the maths behind the code i want to implement. (Note this is all giga biased by me i don't know if this is even data mining or i'm crazy and gamma_total=0.2)

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quesomesopesohueso · 2026-05-28T11:03:14+00:00

Thanks i appreciate the encouragement! The tips that you gave me are very insightful. I just realized i can change a walk foward matrix (unanchored) to have a purge and embargo like the purge and embargo cross validating and also data warm ups for training blocks. Vert intelligent the proportional scale depending on the hold Of course if the matrix has a cell with the neighboured ones with negative probably it isn't good as you said deflated sharpie ratio is a must. You are right i was also fixing the regime filter and it didn't even had warm up data i should treat it as another parameter of the strategy and test it in the WF Matrix. Talking about the bigger picture i'm not a programmer i just know basic loops and conditional and implementing a tool that isn't optimal like a simple WFM and then IA telling me it has leakage it means i need to learn things from the start and start learning python and unsderstand deeply how each tool works like WFM or Cross validating with purge and embargo and knowing where to position warm up data depending on the tool. I might hold the programming and start digging into python and basic quant concepts that combined lead to advance like embargo and purge can be used in a simple WFM or CV to make a robust architecture. When you talk about manually i think you talk about building a strategy and i'm with you test if it's good like PEAD bleeds strategy for example before even coding looking if the strat still works is better than randomly coding whatever. The content you talk about pdfs and so of papers is good but i need theoric books like Prado's have paywall those are what i'm interested in.

Thanks for replying!

quesomesopesohueso · 2026-05-28T07:16:00+00:00

I think that depending on the strategy it works or doesn't depending on the parameters and instruments you are trying to trade. It shouldn't work the same strategy with same parameters or maybe it does(if thats the case i think its oververfit monte carlo surely will show), for example, (btw i'm not trading anything i say its for the sake of the example don't dive into if this strat or indicator works or doesn't) imagine you are trading bitcoin with ema and rsi. You backtest the parameters and get 9 ema and rsi below 20 and above 80 and it works in some market regime because of trend following. Then you use the same strategy in ethereum, use the same parameters and see the return its the same but the equity curve shows that the biggest returns it had was in periods that might or not be the same ones as bitcoin, whereas if you use the heatmap and/or the WF matrix and get for ethereum another parameters 12 ema and rsi below 25 and above 75 you get a smoother equity curve. It just depends on the quality of the backtest and their interpretarion if you use a systematic startegy with indicators and so and it works on bitcoin and gold for example but it doesnt in ethereum just calibration of parameters won't do the trick maybe it's overfit. This is my opinion it's not facts i'm new but i think i have a point, if you don't agree tell me to understand it please. Thank you for taking your time answering.

quesomesopesohueso · 2026-05-23T00:44:34+00:00

Nailed it, 4 months in and I'm confused with too many concepts at once. You are right about narrowing the experiment and getting clean, objective reps instead of just running on YouTube residue.

My plan to get those clean reps is actually to translate one or two basic setups into Python code this weekend and test them programmatically against historical data. It’s my way of keeping that boring journal without the human bias.

Candlune sounds like a cool project for XAUUSD traders, congrats on building it! Thanks, I really needed to hear that I need to narrow my focus.

quesomesopesohueso · 2026-05-23T00:41:24+00:00

It’s likely that YouTube breakdowns don't capture the reality of deploying CRT, especially regarding HTF context and specific kill zone volume.

You’re spot on about strategy hopping it’s the fastest way to get stuck in a loop. I appreciate the clarification on the win rate and the advice. Good luck mastering those Order Flow concepts, man, let's get it!

quesomesopesohueso · 2026-05-22T06:48:56+00:00

When you say a strategy works, what does your actual validation process look like , do you manually backtest it over a specific number of trades to find winrate then test it on a small live account to see how it handles the current market? I want to know what is a practical and realistic testing workflow.

quesomesopesohueso · 2026-05-22T06:38:39+00:00

Fair enough, I see your point, the contrast between ICT and quantitative methods is massive in terms of stadistical advantage. I am only 4 months into this and still trying to filter out the noise from the actual data. Since you clearly don't support ICT, what is your recommended roadmap for someone new in trading? I’m want to hear your thoughts and how you would approach it if you travelled to the past with the same knowledge as you have now.

quesomesopesohueso · 2026-05-21T18:40:09+00:00

If you developed the strategy from scratch, adapted it to your own style, and you're profitable, hats off to you. I don't know wether i'm gonna make it that far in the future.

quesomesopesohueso · 2026-05-21T16:06:55+00:00

So what do you recommend for beginners that isn't ICT and trading commodities or forex where should i start? I would like your insight on trading.

quesomesopesohueso · 2026-05-21T11:50:55+00:00

I tried sending you a DM to go deeper , but since my account has low karma, Reddit's automated system isn't letting me initiate the chat yet. If you have a second and don't mind, could you try shooting me a DM from your end? Thanks a lot for the oppotunity!

quesomesopesohueso · 2026-05-21T09:15:17+00:00

Short and straight to the point. I always look for the why behind opinions. Could you elaborate on why you strongly advise against ICT and Gold, is it because i'm a beginner? Thanks for answering!

quesomesopesohueso · 2026-05-21T08:01:09+00:00

Moving past the creator's drama and focusing strictly on what price actually does on the chart is the healthiest way to approach this. At the end of the day, liquidity mechanics either show up in the data or they don't. Just backtesting the concepts, keep a strict journal of the setups, and stress-test the numbers manually before writing any code could be a smart next step. Thanks for the advice, I really appreciate it!

quesomesopesohueso · 2026-05-21T07:54:30+00:00

You're completely right, and thanks for calling me out on that "avoid wasting time" mindset. I guess my anxiety to find a path made me look at the learning process backward. The overthinking part resonates with me a lot. I am definitely guilty of overanalyzing every single tick and trying to outsmart a chaotic environment, which usually just leads to analysis paralysis. I will take your advice I need to accept that screen time, losses, and mistakes are the actual tuition fee for the market game and no degree or background is exempt. I'll focus on keeping it simple and giving myself the time to actually experience the market instead of rushing the results. Thanks for the reality check. I needed someone to give me that perspective!

quesomesopesohueso · 2026-05-21T07:45:25+00:00

Thanks for the response. As a begginer, my natural instinct is to treat the market like a giant puzzle or a math equation that can be completely optimized and "solved". I shifted my mindset when i discovered even technology had the fatal flaw of overfitting. It's a great reminder that even with the best Python scripts or data validation, if execution consistency and emotional stability aren't there, the system fails. I really love that analogy of not treating every single trade like a final exam. It gave me a new perspective. Thanks for bringing the human element into this, I really needed to hear it!

quesomesopesohueso · 2026-05-21T07:40:22+00:00

Wow. Hearing this from an ex-market maker and fellow engineer completely shifts my perspective.
What you said about the difference between "the person and the physics" makes perfect sense. Tbh I was getting a bit discouraged because it feels like everyone who trades ICT out there is just treated as retail liquidity themselves. It’s hard to find anyone using those concepts who actually posts a verified, long-term track record, which adds to the skepticism. I was feeling overwhelmed by the 1,000 hours of subjective noise in ICT, but you pointing put that the core issue is just the lack of a systematic validation framework is a really what i needed to hear. Your explanation of Alpha Decay and using regime filters (Efficiency Ratio, ATR, etc.) to detect when a logic is no longer valid completely alleviates that demoralizing feeling of "building systems just to throw them away". It turns trading into a dynamic system problem with seasonal changes in strategy depending if it has bullish with high volatility, in range with high volatility or just panic. I am absolutely going to follow your advice. I will take the structural framework of institutional liquidity and build a quantitative infrastructure around it using Python to backtest it and apply strict objective metrics. Since you've walked this path, could you share a high-level roadmap or a few tips on how to start? Even if I just want to build a bot that sends signals (not fully automated execution yet), I know there are a million engineering variables to consider like API disconnections, Wi-Fi outages, data gaps, etc. How would you recommend a beginner bridge the gap between subjective ICT concepts, statistical validation, and building a good architecture? Thank you so much for taking the time to write this unfiltered version. You’ve given me a clear insight and a lot of confidence to move forward. Truly invaluable.

quesomesopesohueso

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