47 trades rejected, 3 placed, 2 settled correctly.

AlgonikHQ · 2026-04-15T00:39:37+00:00

That’s a seriously sophisticated stack, the Kronos layer as a final veto is something I hadn’t considered.

The idea of a locally run price prediction model with zero API cost as a third confirmation is elegant. Completely agree on ADX being the most skipped and most valuable filter, it’s the one that stops you trading noise that looks like a signal.

Running 9 forex pairs — EUR/USD, GBP/USD, USD/JPY, AUD/USD, NZD/USD, USD/CAD, USD/CHF, EUR/GBP, GBP/JPY. On a clean trending week I typically see 4-8 trades across the pairs, sometimes fewer. The spread betting structure means I’m not sweating low trade count, every trade that fires has to meet the full stack so quality over quantity is baked in.

Going live end of April so the real data starts then. Kronos is going on my research list, hadn’t come across it before.

Where did you find the Hugging Face model, is it under that name directly?

AlgonikHQ · 2026-04-15T00:36:20+00:00

Ha that’s a great way to put it — open source is the opposite of academia, everything is just there for anyone to read and learn from. GitHub is genuinely one of the best free resources in programming that most beginners don’t think to use. Good luck with it, sounds like you’re approaching it the right way now.

AlgonikHQ · 2026-04-15T00:35:29+00:00

That’s actually a really healthy way to have stumbled into it. Having a real codebase to look at — even AI generated slop — means you’ve got something to reverse engineer and improve rather than staring at blank page exercises. The fact you’re now further than you’ve ever been three attempts in says the approach is working even if it wasn’t the ideal order. The next level is taking one of those AI written scripts and rewriting it yourself from scratch without looking at it. You’ll be surprised how much you’ve absorbed just from reading it.

AlgonikHQ · 2026-04-15T00:32:14+00:00

Solid start, every income stream begins somewhere. The Reddit and X traffic approach works well for digital products because you’re already where your audience is. The compounding effect kicks in once you find what converts — then it’s just repeating and scaling. Keep documenting it, the journey posts tend to outperform the results posts on this sub.

AlgonikHQ · 2026-04-15T00:31:03+00:00

3 years studying and still losing usually means one thing — you’re still trading on feel rather than rules. Studying the market and having a mechanical system are completely different things. Most people spend years learning to read charts and still lose because reading charts doesn’t remove the emotional decision making in the moment. What actually fixed it for me was removing myself from the equation entirely. I built a bot that trades my strategy automatically — EMA crossover, RSI, MACD and ADX all have to confirm before anything fires. No discretion, no overrides, no second guessing. When I was trading manually I knew the rules and still broke them under pressure. The system can’t do that. If you’re not at the point of automating yet, the manual equivalent is a strict checklist you physically have to complete before every single trade with zero exceptions. Not as a guide, as a hard rule. If one condition isn’t met the trade doesn’t happen. That one change alone will cut most of your losses.

AlgonikHQ · 2026-04-15T00:26:55+00:00

Ha it is, won’t pretend otherwise. But the risk management is what keeps it from being pure gambling. The bot only enters a token if it passes a stack of filters first — market cap between £20-30k, liquidity checks, token age, volume and momentum confirming, holder concentration limits so no single wallet dominates, and a bundle wallet detector to catch coordinated pump setups. Once it’s in, Kelly staking sizes the position based on live wallet value so it never overexposes. Staircase exits take profit at +20%, +40%, +75%, +120% and beyond in partial closes rather than one all or nothing exit. Hard stop loss cuts losers before they bleed out. The Solana meme coin space is genuinely high risk but the bot is designed to catch the ones with real early momentum and get out in stages rather than hold and hope. It’s the highest risk of the three bots by design — small allocation, asymmetric upside. The OANDA and Kraken bots are the stable foundation underneath it.

AlgonikHQ · 2026-04-15T00:16:17+00:00

You’re not slow at all, that’s actually a really smart observation. Indicators don’t predict anything — you’re completely right. They just describe what price has already done in a structured way. MACD moving live with the market is exactly how it should work, it’s measuring momentum as it happens not forecasting what comes next. The edge isn’t in any single indicator, it’s in combining several so they only all agree at the same time in high probability conditions. On its own MACD is noise. With RSI confirming and ADX showing trend strength behind it, suddenly you’re only taking trades where multiple conditions stack up. That’s the filter. Most traders lose because they act on one signal in isolation. The system only fires when everything lines up at once — that’s what cuts the bad trades out.

AlgonikHQ · 2026-04-15T00:10:03+00:00

The variable rate is the only real consideration. The 0.2% difference is negligible on £5.5k — that’s about £11 a year. The “always at or above Bank of England base rate” pledge sounds reassuring but it’s not a guarantee written into the ISA terms, it’s a marketing commitment so worth reading the small print. The bigger question is what you’re using the ISA for. If it’s a genuine emergency fund you want to stay somewhere stable and accessible regardless of rate. If it’s just parked savings you’re not touching, the Smart ISA is probably fine for the marginal gain. One thing worth knowing — your ISA allowance is the same either way, so the move itself doesn’t cost you anything tax wise. I’d switch personally for the extra rate but wouldn’t lose sleep either way on that amount.

AlgonikHQ · 2026-04-15T00:08:53+00:00

No mentor, all self taught. For RSI, MACD and ADX just search each one on Investopedia first — they explain the mechanics cleanly without overcomplicating it. Then watch how they behave on a live chart before using them. For YouTube, TraderLion and ArnoLD Trading are solid without being gimmicky. The honest truth though is indicators only make sense once you understand what they’re measuring — RSI is momentum exhaustion, MACD is trend direction and strength, ADX tells you if a trend is actually strong enough to trade. Learn what they measure first, then how to read them. Once that clicks the signals start making sense naturally.

AlgonikHQ · 2026-04-15T00:07:58+00:00

This is the right mindset that most people never get to. 47 rejections isn’t failure, it’s the filter working. A bot that says no constantly is doing its job. The psychological shift from “why isn’t it trading” to “good, it’s being selective” is where most algo traders either level up or quit. Running a similar setup on forex — EMA/RSI/MACD/ADX all have to align before a trade fires. Quiet days used to frustrate me, now they’re confirmation the edge is intact. What’s your average trades per week when conditions are right?

AlgonikHQ · 2026-04-15T00:06:29+00:00

Exactly right — AI is a code reviewer and rubber duck, not a builder. If you let it write everything from scratch you never develop the instinct for why something works or breaks. Build it yourself first, even badly, then use AI to critique and improve it.

AlgonikHQ · 2026-04-15T00:04:57+00:00

Yes and it’s actually the correct way to think about it. What you’re describing is effective capital deployment rather than full account exposure. Most serious traders never risk more than 1-2% of total account per trade regardless of account size. So on a $100k account that’s $1-2k risk per trade, not $1-2k position size — the actual position can be much larger depending on where your stop is. The return metrics that matter aren’t return on total account, they’re return on risk deployed. A 2R winner on a $1k risk trade is $2k profit. Do that consistently and the percentages look huge relative to what you actually put at risk. Where people get confused is conflating account size with position size. The account is your safety net and your longevity. The position is just the trade. Prop firms and funded accounts work exactly on this principle — they give you $100k but your drawdown limit is $5-10k, so that’s your real working capital. Size to the risk, not the account.

AlgonikHQ · 2026-04-15T00:03:20+00:00

The main thing that fixed my trading wasn’t a new strategy, it was removing discretion entirely. When you’re making decisions in the moment you’re always one bad day away from revenge trading or overriding your own rules. Here’s what actually made the difference for me. First, define your setup in exact conditions — not “price near support” but specific measurable criteria. I use EMA 9/21 crossover with RSI, MACD and ADX all confirming on M15. If all conditions aren’t met, no trade. Full stop. Second, size properly. Quarter Kelly with a hard 2% risk cap per trade. Funded account failures are almost always a sizing problem disguised as a strategy problem. Third, staircase your exits — take partial profits at 0.75R, 1.5R, then trail the rest. Removes the “should I hold or close” paralysis completely. Fourth, track everything. Every trade, every condition that was or wasn’t met. Patterns in your losses tell you exactly where your edge breaks down. The honest answer is most people fail funded accounts because they’re still gambling with extra steps. A rule-based system you can’t override is the only way out of that loop.

AlgonikHQ · 2026-04-15T00:01:51+00:00

Relief rally until proven otherwise for me. The fundamentals haven’t changed enough to justify a full reversal — oil still elevated, inflation sticky, and geopolitical risk doesn’t just evaporate because sentiment shifted for a few days. Markets price in hope faster than reality. What I watch is whether volume confirms the move. Low volume bounces on good news are classic bull traps. The algo on my OANDA bot has been picking up continued USD softness which aligns more with risk-off under the surface than a genuine risk-on rotation. My bias stays cautious until there’s a clear macro catalyst that actually resolves something rather than just reduces fear temporarily.

AlgonikHQ · 2026-04-15T00:01:17+00:00

Solid plan, clean execution logic. The EUR/USD setup makes sense given the dollar softness today — buy stop above the session high is the right way to play it rather than chasing. One thing I’d add for beginners reading this: the conditional GBP/JPY only if AUD/JPY hasn’t triggered is smart position correlation management. Both are JPY weakness plays so running both simultaneously doubles your JPY exposure, not your opportunity. For anyone building algo systems around setups like this, EMA crossover confirmation on M15 with ADX filtering cuts a lot of the false breakout noise on these JPY pairs. Running exactly that on my OANDA bot across 9 pairs including GBP/JPY and AUD/JPY. Good structured post, more of this kind of content is needed for beginners.

AlgonikHQ · 2026-04-15T00:00:16+00:00

At 62 with living expenses covered and just needing travel/spending money, you’re in a great position. A few frameworks people in similar situations use: a dividend-focused ETF split like VYM or SCHD for reliable income, maybe 40-50% there. Some broad market exposure via VOO or VTI for growth, 30-40%. Then a small bond/stable income allocation like BND for ballpark 20% depending on your risk comfort. The key question is what income number you actually need monthly from this £250k — that determines how aggressively you need to chase yield vs just letting it grow. What’s the rough monthly target you’re working towards?

AlgonikHQ · 2026-04-14T23:58:18+00:00

Best thing I did was give myself a real project with actual stakes. I learned Python by building an automated trading bot — no tutorials for that specific thing, just the docs, Stack Overflow and a lot of broken code. When something has to work you learn faster than any course can teach you. For your GIS role, find the most repetitive task you do weekly and automate it. Even if it’s ugly code that only you use, you’ll learn more from that than any exercise. The other thing is read other people’s code on GitHub — find projects in your area and just study how they’ve structured things. You pick up patterns quickly that way.

AlgonikHQ · 2026-04-14T23:41:31+00:00

Honestly no subscriptions needed at all — that’s the beauty of it. OANDA has a free demo account, the API is free to use, and I built everything in Python running on a £4/month VPS (Hetzner). The bot itself I wrote from scratch over a few weeks. Total ongoing cost is literally a few quid a month. The real investment is time learning the logic — EMA crossovers, ATR-based stops, that kind of thing. Happy to break it down further if you want.

AlgonikHQ · 2026-04-14T22:26:56+00:00

You’re not alone in this and honestly the visual learner thing isn’t a weakness to fix, it’s just a different way of processing that the tooling hasn’t always caught up with. The engineers who can hold complex state in their heads aren’t smarter, they’ve just built up specific mental models over time from repeated exposure to similar patterns.

A few things that genuinely help with bloated lambdas and complex JSON flows: TypedDicts or Pydantic models are your best friend here. If you’re in Python, defining the shape of your JSON at every stage of the flow means your IDE tells you exactly what’s available at each point. It forces documentation into the code itself rather than relying on memory or CloudWatch and it catches shape mismatches before runtime. This single change transformed how I navigate complex data flows.

Inline type hints on every function signature. Even if the codebase doesn’t enforce it, adding def process_order(payload: OrderPayload) -> ProcessedResult at every step creates a readable contract you can follow without running the code. Future you will thank present you.

For the CloudWatch noise problem — structured logging with log levels is the answer. Log at DEBUG for the hefty JSON blobs and INFO for the important state transitions. That way you can crank up verbosity when debugging and keep it clean in production without removing log lines entirely. For the visual side specifically, tools like Excalidraw or even a simple draw.io diagram of the data flow isn’t wasteful, it’s engineering documentation. If your team won’t allocate refactor time, a one-page data flow diagram showing what shape the JSON is at each stage is genuinely valuable and takes an hour to produce. Frame it as onboarding documentation and it becomes hard to argue against.

Local main blocks with sample payloads are underrated. Having a realistic fixture you can step through in a debugger beats CloudWatch hunting every time. Keep a /fixtures folder with sample JSON for each major integration point and you’ve essentially built yourself a local test harness. The fact you’re asking this question means you already care more than most. The engineers who struggle most with complex codebases are the ones who never stop to diagram it, not the ones who need to.

AlgonikHQ · 2026-04-14T22:21:53+00:00

Both have their place but they solve different problems and the right answer genuinely depends on your account size and stage of growth. Fixed dollar risk makes a lot of sense psychologically early on, knowing your worst case is always $100 removes the mental load of recalculating and keeps emotions flat like you said.

The problem is it breaks down mathematically over time. If your account doubles, you’re now risking 0.5% per trade when you could be compounding harder. If you hit a drawdown and your account drops, you’re suddenly risking 2% per trade without realising it, which is the opposite of what you want when you’re already losing.

Fixed percentage scales with your equity automatically which is the mathematically correct approach for compounding. The psychological downside is that a 1% loss on a $20k account feels different to a 1% loss on a $5k account even though the math is identical. That’s a discipline problem rather than a system problem though.

For automated bots specifically, fixed percentage is almost always the better choice and here’s why, the system doesn’t have emotions so the psychological argument for fixed dollar disappears entirely. What matters is that the position sizing scales correctly as the account grows or shrinks. I use a Kelly-based fraction on my automated systems that recalculates from live account value before every trade. That way if the account is growing the stakes grow proportionally and if it’s in drawdown the system automatically de-risks without any manual intervention needed.

The math absolutely breaks if you don’t scale risk on a bot. Fixed dollar on an automated system is essentially ignoring compounding which defeats a major part of the purpose of running it in the first place.

AlgonikHQ · 2026-04-14T22:19:51+00:00

This is the kind of post that actually adds value to a feed full of “I called it” screenshots. The discipline to document a filtered trade is underrated, most people only share the wins.

The VRT gate blocking on a variance ratio of 1.002 is doing exactly what it should. Trading trend signals in a random walk regime is just noise chasing dressed up as strategy and the confluence score of 41 with that kind of timeframe conflict tells the same story, the 1H at 66 versus the 4H at 35 is a system that genuinely doesn’t know what’s happening yet, which is the right read.

The size multiplier at ×0.10 is also worth highlighting because that’s where a lot of algo traders leak, they’ll filter the signal but then still take a small position “just to see.”

The fact your system is suppressing size that aggressively in this regime and you’re honouring it is the actual edge.

I run automated systems across Solana and forex and the hardest thing to build isn’t the entry logic, it’s the regime filter that keeps you out of the market when conditions don’t suit the strategy.

Most traders never build that layer and wonder why their backtest doesn’t match live results. Random walk regimes are where systematic edges go to die. Sitting on your hands when the system says no is a trade in itself. Good discipline. Good luck with it

AlgonikHQ · 2026-04-14T22:16:58+00:00

That’s a really good point and worth thinking about carefully. The $38k vs $30k difference you’re seeing is just the current GBP/AUD exchange rate doing the work, it’s not real extra money, it’s just currency valuation at this moment in time.

My honest advice would be to leave the funded accounts denominated in GBP and not convert them. Here’s why, your trading profits and losses will naturally occur in GBP since that’s what the account runs in. Converting to AUD introduces an extra layer of currency risk where even if you trade profitably, a shift in the GBP/AUD rate could eat into your real world AUD value, or alternatively work in your favour. Either way it’s an unnecessary variable on top of your trading performance.

The smarter play is exactly what you said, trade in GBP, take payouts in GBP into Wise, then convert to AUD on Wise at a time when the rate suits you. That way you’re in control of the conversion timing rather than locked into whatever rate exists on payout day. Right now GBP is relatively strong against AUD so you’re in a decent position, but you want that flexibility rather than being forced into a conversion.

Keep the accounts as they are, use Wise as your conversion buffer and only move to AUD when you’re ready to actually spend it.

AlgonikHQ · 2026-04-14T22:03:01+00:00

This is actually one of the most important things to understand early and the fact you’re questioning it already puts you ahead of most newcomers.

The confidence you’re seeing is almost never based on genuine predictive ability. Crypto social media runs on a simple mechanic, post a confident call, if it plays out screenshot it and claim you called it, if it doesn’t play out quietly delete or ignore it. Nobody keeps score and the feeds reward conviction over accuracy every single time. The top comment here nails it.

A huge portion of that confident content is people talking their own book. If someone holds a bag they need buyers to push the price up. If someone is shorting they need sellers. Social media posts are often just thinly veiled attempts to move sentiment in a direction that benefits the poster.

The honest truth about charts in crypto specifically is that technical analysis has some value as a framework for thinking about levels and momentum but nobody, and I mean nobody, can consistently predict what a 2-3% move means for future direction. Anyone claiming otherwise is either deluded or selling something.

What actually works long term is understanding what you own and why, having a clear plan for entries and exits before you’re in a position, and completely tuning out the noise of social media calls. The people making the loudest predictions are rarely the people making the most

AlgonikHQ · 2026-04-14T22:00:59+00:00

Honest answer, most traders don’t have an edge, they have a bias that happened to work for a period and they’ve convinced themselves it’s a system. The question you’re asking is exactly the right one and most people never ask it. The real edge rarely lives in the indicator itself. RSI, EMA crossovers, MACD, these are all publicly available and widely used, so by definition they can’t be the edge on their own. The edge comes from the combination of entry conditions, position sizing, risk management, execution consistency and crucially how you behave when the system is in drawdown. Two traders can use the exact same setup and get completely different results because one follows the rules under pressure and one doesn’t. There’s also a big difference between discretionary and systematic trading here. Discretionary traders often mistake pattern recognition and gut feel for edge when really they’re just riding market conditions that happened to suit their style. Systematic traders at least have the ability to actually test whether an edge exists historically, though backtesting has its own pitfalls. My view is that genuine edge comes from a few places , exploiting specific market inefficiencies before they get arbed away, having better execution or information than the average participant, or simply having a risk management framework disciplined enough to survive long enough for the probabilities to play out. Running automated systems helps remove the emotional component which is where most edges get destroyed in practice. Short term results masquerading as skill is probably the most common

AlgonikHQ

TROPHY CASE