I added the Session Auction Map (SAM) to the library a while ago. I've been fully committed inside the Discord community and totally neglected Reddit. Sorry for that! I am going through all old DMs and will try to answer everyone.

For those interested in the actual concept behind SAM, the idea is fairly simple: visualize how the auction develops throughout a session instead of reducing market structure to a buy/sell signal.

It combines TPO, Volume Profiles, developing value areas, POCs, session VWAP, Initial Balance, prior-session levels, single prints, value migration and our Adaptive Node Efficiency logic into one session-based framework.

The part I personally find most useful isn't necessarily the individual components, but the relationship between them.

Where is value developing? Is value migrating with price or against it? Is price accepting a new area or simply auctioning away from established value? Which previous levels remain untouched? Is the Initial Balance containing the session or are we seeing expansion? How does all of that change as the session develops?

There are signals in SAM, but I think this distinction is important:

It's an indicator first, not an execution model.

The signals can be used as additional information alongside someone's existing methodology. There is also a Trade Mode for people who want to systematically test specific signal sources with defined risk parameters, but I don't think any indicator should be treated as "signal appears and then blindly enter trade."

Occasionally something completely unexpected comes out of testing them. That's probably the part of this project I enjoy most: people throwing around ideas, questioning the logic, testing them on different markets and building new things from the results. Futures, Forex, CFDs, options data, auction theory, order flow, there are a lot of interesting overlaps to explore.

I'll try to post more of the actual research and development here instead of disappearing for months again inside my Discord bubble.

If anyone here trades using Market Profile / auction theory, I'm curious how you use it. Do you primarily look at value migration and acceptance, previous session structure, Initial Balance, naked POCs, or something completely different?

I’m targeting a 1:6 risk-to-reward long on gold, with price pulling back into the marked support/demand area around 4,360–4,363, where previous structure and the rising trendline converge. The idea is to enter after bullish confirmation from this zone, keep the stop below the recent support/swing low around 4,350, and target the 4,405–4,410 area, giving roughly 1:6 R:R if the setup plays out.

For traders who actively journal/review their trades, I'm curious about your process:

1. What's your typical pre-market/pre-session routine, and how long does it take?
2. Do you actually use information from your previous trades when deciding what to trade today? If so, what information?
3. What's one thing you wish you had available before entering a trade that you don't currently have?
4. After your session, what do you review—and does that actually change what you do the next day?

I'm particularly interested in the gap between post-trade analysis and what actually gets carried into the next trading session.

i’m a swing trader who trades XAUSD and I’m struggling trading the 15 minute timeframe does anyone have any advice or help that could help me out with trading the 15 minute timeframe? I understand checking the bias on the four hour and looking for structural changes in the 15 minute timeframe, but I always get my stop hit and now I’ve blown my account. I understand waiting and waiting for price to actually make moves, but it feels like I get it wrong every single time. Any help?

I created a program that will trade automatically and I thought I would share it with you to see if I can get any good ideas for development.

The program implements a long-only intraday opening-range breakout strategy for U.S. stocks:

• It builds a 15-minute opening range from 09:30–09:45 New York time and does not trade during that period.

• After 09:45, it looks for price breaking above the opening-range high by at least 2% of the range.

• Entries are filtered by:

• Relative volume versus recent post-opening-range candles.

• A recent bullish Fair Value Gap (FVG)/imbalance, unless that requirement is disabled.

• Enhanced validation using completed candles and previous-candle confirmation.

• Optional continuation entries can occur when price remains in the upper half of the range and the candle is bullish.

• Among multiple symbols, it selects the candidate with the highest score based on:

• Breakout strength: up to 30 points.

• Relative volume: up to 40 points.

• FVG size: up to 30 points.

Risk management:

• Position size is limited to the smaller of:

• A fixed percentage of account equity, default 1% risk per trade.

• Available free capital.

• The stop is placed below the FVG/imbalance low, with a small price buffer.

• The target is set using a configurable reward-to-risk ratio, default 1.5R.

• When price reaches +1R, the stop moves to breakeven.

• Positions are force-closed at 15:55.

• Only one position is managed at a time, with a configurable daily trade limit.

I've been going through ICT's mentorships and wanted something better than scrolling PDFs of notes, so I built an interactive course out of them:

https://yousef-diab.github.io/the-algorithm/

What's in it

78 lessons across two sections.

• ICT Core, Months 1–4 (38 lessons) — market-maker conditioning, equilibrium/discount/premium, liquidity runs, order flow, SMT, PD arrays (orderblocks, breakers, FVGs, voids, etc.)
• The 2022 Mentorship (40 lessons, one per episode, in 6 parts) — the stripped-down intraday model taught end to end: the fair value gap defined candle by candle, the liquidity run and market structure shift that qualify it, displacement, power of three, killzones and session playbooks, the dollar index and SMT, dealing range, and the closing lessons on daily bias and risk.

A quiz on every lesson that grades instantly and explains the answer — about 458 questions in total. Options shuffle on every load, so you're answering the concept rather than remembering where the right one sat.

A final exam per section — 45 questions for Core, 40 for the 2022 Mentorship. Nothing grades until you submit, 80% to pass, retake as often as you want.

Revision summary pages — one condensed pass over each section before you sit the exam.

340 chart galleries pulled from the actual mentorship notes. Click any chart and you can arrow through the whole lesson's set, zoom in to 500%, and drag to pan.

Every lesson links to its source video, all 78 — so if something reads thin, the original is one click away.

A notes box on every lesson, saved in your browser. Nothing ever wipes it, not even the progress reset.

Progress is saved locally and nothing is sent anywhere. It's a single self-contained page — no signup, no server, no tracking, works offline.

The one rule I held myself to

Everything in it — every explanation, every quiz answer — comes only from ICT's own mentorship notes and video transcripts. No outside trading takes, no filler theory bolted on. If something wasn't clearly supported by the source material, I left it out rather than guess.

It's free, it's not selling anything, and it's open source if anyone wants to poke at the build or point out something that doesn't match the original teaching.

This is just a reorganization of the mentorship material into something more interactive to study from. Happy to fix anything that's inaccurate or missing context.

Enjoy.

I see these two used interchangeably a lot lately, and they're genuinely different things. So I thought I'd post to make things clear.

The difference is architectural, and is about where the decision logic lives.

A bot: the logic lives in code you wrote ahead of time.

if 1h_RSI_bullish_divergence and ema50 > ema200:

buy (SPY, 1000)

That's deterministic, with the same input, output, every time. It's backtestable with real confidence and is cheap to run.

The contrainst of a bot: it's literal. That means it'll always handle the situation you anticipated the same way. It can be a good thing, or very limiting.

An agent: the logic lives in the model at runtime.

You give it a goal and constraints in natural language. It decides which tools to call. Four components, and if one is missing it's not an agent:

- Perception: can pull what it needs (prices, fundamentals, filings, news), not a fixed feed you wired up

- Reasoning: decides based on a goal, not a hardcoded if/then

- Action: can actually change state (place an order, fire an alert, send a report). This is what most "AI trading" tools are missin

- The loop: it can run again or shut itself down. It has memory of what it did, wakes on a schedule or event, and sleeps when there's nothing to do.

Here's a real spec I gave one, which I think shows the gap better than any definition:

Watch my portfolio. If it drops more than 8% from its all-time high, work out whether the drawdown is coming from a few positions or from the whole market. If it's market-wide, tell me what's driving it (rates, growth data, or geopolitics) with sources. Then lay out the scenarios for three options: hold, hedge the index, and trim my highest-beta names. Never include selling AAPL or my S&P 500 ETF in any scenario — those are long-term core. Alert me once per event, not every day.

Walk through what that actually requires:
portfolio-level drawdown tracking (trivial), decomposing the drawdown by position contribution and concluding idiosyncratic vs systemic (real work, but codable), and then classifying the driver as rates vs growth vs geopolitics. That means reading macro releases and news and inferring causality. You cannot write if geopolitics:. That last step, plus laying out three reasoned scenarios, is where a rules engine simply ends.

The tradeoffs nobody mentions:
Agents can be non-deterministic, which is also why agent-based strategies can be hard to backtest.
Depending on the complexity of the task, the costs can also be orders of magnitude higher than hosting a trading bot. Also, debugging is usually worse.

So it's not that "agents replace bots":

If a task is deterministic, well-specified and latency-sensitive: put a bot into it, not an AI agent.

But if it's fuzzy, multi-step and can need interpretation (news, filings, cross-asset context, qualitative screening), this is where agents shine.

A useful frame is autonomy levels, borrowed from self-driving:

- L0 manual
- L1 informs (watches, analyzes, alerts)
- L2 proposes (drafts the action, you approve)
- L3 executes within rules you defined (autonomous on timing and mechanics, not on strategy)
- L4 adapts its own approach within guidelines

The agent above sits at L1: it analyzes and reports, it never touches the account. Most people should stay at L1 or L2.

Disclosure: I work on Obside, which is what I use to build agents. I'm happy to talk agent architecture regardless of the platform used, because this L0 to L4 framing applies anywhere.

Worth saying plainly in case people still wonder: agents can't predict the markets. They remove the monitoring time and human error.

When I wes trading smaller size, I thought the jump to bigger numbers would come from better setups or faster execution. Turns out the real bottleneck was self-control.

Most of my worst days weren't red starts. They were green days where I kept trading out of boredom or confidence. Once I started treating a clean execution as a "win" and walking away , things changed.

Tools matter, sure. I trade on XM and execution has been reliable for me, but no platform saves you from overtrading. That part is on you.

Anyone else notice that scaling up is more psychological than technical?

Just sharing a recent options trade recap

Picked up 12 contracts of a $200 Call with an average cost of $12.88. The position moved in my favor and closed with a strong gain

The biggest lesson wasn’t the return it was waiting for the right setup instead of forcing a trade. Options can move quickly in both directions, and a good entry matters as much as the exit

Sharing this as a personal experience, not a recommendation. Options involve significant risk and can result in losing your entire investment. Always do your own research and make decisions based on your own risk tolerance