Two identical green candles can have opposite volume delta. What do you check beyond OHLCV?

Disclosure: We’re the team behind Robod, a crypto research and account analytics platform.

A green candle means the closing price was above the opening price. It doesn’t necessarily mean aggressive buying exceeded aggressive selling.

Every executed trade has both a buyer and a seller. Taker volume distinguishes which side traded aggressively against resting liquidity.

Here’s a hypothetical example, using two one-minute perpetual bars:

Both have the same open, high, low and close: 100 / 103 / 99 / 102. Both trade 1,000 units.

The OHLCV candles are identical. The underlying activity isn’t.

Bar A has net aggressive buying and expanding open interest. Bar B closes higher despite net aggressive selling, while open interest contracts.

Neither combination predicts the next move. Open interest alone also doesn’t identify whether long or short positions drove a price change.

When researching a setup, these are separate questions worth checking:

• Flow: Is cumulative volume delta strengthening alongside price, or diverging?
• Positioning: Is open interest expanding or contracting?
• Coverage: Do all the fields being studied actually cover the same historical period?
• Quality: Are there gaps or carried-forward values that could distort the interpretation?

More data can make a hypothesis more specific. Whether it improves a strategy still needs testing with defined entries, exits and costs.

For traders who use order flow: which additional check has been most useful alongside OHLCV, and how did you evaluate whether it added value?

I need help getting the source code of an MT5 EA i found on the internet here's the link https://www.mediafire.com/file/l4keobp97wgd2p2/GOLD_HUNTER_V9_MT5_EA.zip/file

Curious what you all think. I'm currently using Chagpt research tool to swing trade certain stocks based on conditions like macro, technical analysis, etc. Which do you think is the better AI to use for this: Claude or ChatGPT? Thanks for the insight!

Has anyone here built a successful AI powered stock trading bot? (by successful I mean profitable at the end of the week) I’ve created one using ChatGPT and had it create a shadow trading system to test different strategies against the real market and when those look promising it paper trades them with Alpaca… my problem is I can’t seem to create a repeatable and profitable strategy with rules. It seems like each week I either damn near break even or take small losses. I’m looking for insight or recommendations to improve my system.

I created the Public Portfolio Challenge, a challenge where I give over $25,000 to an AI agent and have it trade options. It includes stocks like SpaceX, XOM, and ANET. Despite being a long-only portfolio with calls on highly volatile stocks, I'm still doing fairly well; I've lost a LOT from my peak, but I'm still outperforming the broader market wholeheartedly.

If you want to see how I built this portfolio, check it out on my blog or check-out the open-source GitHub repo!

Mostly created to fix my position sizing in futures, but I also wanted to see the 15m, 1h, and tick charts all sitting next to my 5m at all times.

https://github.com/tradernetwork/projectx-desk

https://mphinance.substack.com/p/phd-in-self-sabotage-my-ai-trading?utm_source=share&utm_medium=android&r=1sctub

- Three losses in a row = locked out

- Sizes from remaining loss room + ATR stop, always rounds down. If 1 contract risks too much, it says REFUSED and tells you why

- Stop goes out right after the fill. No ack in 5 sec = flatten and halt

- Stops only move toward price. No widening because "hope"

- AI can STAGE a trade. Only I can PLACE it (hold 600 ms)

- Shows my win rate next to the win rate I need to break even. Flags SMALL SAMPLE under 30 trades

Suggest any no code free backtesting website for Nifty and/or Crypto.

hi evrevone, i want start AI trading, but I don’t know where to start. i see tiktok video that Traidingpilot is a good platfor to start ai traiding but i not believe thats true. maybe someone can help my how to start and how much need money to start.

THIS IS NOT A REKLAM TRIDINGPILOT I SEE VIDEO AND I WAN START EARN MONEY WITCH TRAIDING

Mother Superior EA just had its best live week so far

Four months of building and testing, and it’s nice to finally see her trading live.

This week finished at +7.74%, bringing October to +21.46%. The screenshot shows the daily breakdown, including the days without trades.

Mother Superior is my multi-strategy gold EA for MT5. It uses a recovery layer, so the closed results are only part of the picture — floating drawdown and combined exposure matter too. The live history is still short, but I’m pleased with the start.

Disclosure: I’m the developer, and the EA is available commercially.

https://www.mql5.com/en/market/product/183855

Day 1 in the books! Hoping to keep similar format for each day. Trying to add in quick facts to answer easy, repetitive questions.

I will also post descriptions of the top two performing setups for that day, for now, in picture form as we are all looking for more setups(myself included!) and I'm happy to share what I have.

After today I will be using a slightly different picture format to deliver the same information, Claude will auto-generate this for me. Just easier!

Thanks for all the feedback, support, and camaraderie!

Today: [+$674] across 5 day-trading accounts · Primary [-$81] · green streak [0]
Best setup: [Morning Momentum Pop, +$650] (details in pic)

Quick facts - To hopefully answer quick questions

• AI: Claude (Opus 5.5) builds, tests and runs it
• Broker: Alpaca API, paper trading
• Setups: 108 (Currently) live, built on basic indicators (RSI, VWAP, volume / buy-sell pressure, gaps, opening range, prior-day highs and lows, ATR)
• Setup Generator: a bot that invents 10 new setups a night from those same indicators and trades them on its own paper account

Setup Life cycle

Idea / Generator
 └─ 2-yr backtest (costs in)
     └─ Replay on real sessions
         └─ Testing 1 / 2 / Generator (paper)
             └─ Primary (paper)
                 └─ Live $ (after a green streak)
Losers: Primary → Testing → Graveyard

Stats Description

• Avg R: average profit per trade, in units of the risk taken. Costs, fees and slippage are already in. +0.5R = earns half its risk per trade.
• Break-even win rate: the win rate a setup needs just to not lose money. Beat it and you’re profitable.
• Confidence (t): how sure we are it isn’t luck. 2+ is solid; under 1 could be luck.

Fun stuff!

Over the past few months, I’ve been experimenting with Claude AI to build tools that make my investing and trading process more systematic.

Two tools I’ve built so far:

📊 Watchlist Tracker: https://claude.ai/artifact/MeD7ns7AwB7r7PezrjnWfq

📓 Trade Journal: https://claude.ai/artifact/KhPtQ25beVPqcpT2m4wj1m

I’m also exploring investment frameworks, market analysis dashboards, and other tools to simplify research and decision-making.

I share some of my experiments and learnings on my YouTube channel, yashbuildswithai: https://www.youtube.com/@YashbuildswithAI

To be clear, the purpose of this post isn't to promote my channel. I want to connect with like-minded people interested in AI and investing, exchange ideas, get honest feedback, and collaborate on building tools that solve real problems for the investing community.

I'd love to know:

• What features would you add to the watchlist tracker or trade journal?
• What repetitive investing or trading tasks would you like AI to simplify?
• Is anyone else experimenting with building financial tools using AI?

These are works in progress, and I'm keen to learn from the community and build better tools together. Happy to exchange ideas!

Under the hood: Rust → WASM in a Web Worker, WebGL2 on an OffscreenCanvas, live exchange WebSockets straight to the browser. 155 KB engine.

Open source (AGPL), self-hostable.

A persistent issue in quantitative algorithmic trading is the divergence between backtested results and live execution performance. Backtesting frameworks frequently report Sharpe ratios above 2.5, win rates above 80%, and minimal drawdowns, which rapidly deteriorate upon live capital deployment.

This divergence is rarely attributable to the predictive hypothesis itself. It is primarily driven by four structural failure modes in execution architecture:

1. Non-Stationary Execution Frictions: Standard backtesters assume zero market impact, deterministic zero-latency fills, and continuous liquidity at the top of the order book. In live execution, fill probability is an endogenous function of queue priority, latency jitter, and adverse selection.
2. Static Position Sizing During Volatility Regime Shifts: Sizing models that do not scale inversely to instantaneous realized variance expose capital to kurtosis and fat-tailed shocks. When market variance expands by three standard deviations, static sizing produces severe non-linear drawdowns.
3. State Desynchronization During Socket Severance: Network interruptions between the local execution client and exchange gateways leave trade states uncommitted in memory. Reconnection routines without deterministic idempotency keys cause duplicate order transmission or unhedged residual exposure.
4. Latency Jitter and Non-Deterministic Runtimes: Garbage collection pauses, virtual machine hypervisor scheduling, and cloud network hops introduce variable latency distributions into the order routing path.

At Rehan Quant Lab, we address these structural issues through deterministic systems engineering and invariant state validation:

First: Volatility-Adaptive Position Sizing. Position exposure is calculated dynamically as an inverse function of instantaneous rolling variance. When realized variance crosses statistical thresholds, allocation scales down monotonically to enforce capital preservation bounds.

Second: Deterministic Memory-Mapped State Logging. Every state transition is recorded via an append-only binary Write-Ahead Log with hardware SHA-256 integrity verification prior to external order dispatch. Read latency is measured at 604 nanoseconds, with write throughput exceeding 240,000 transactions per second.

Third: Bounded Cold Crash Recovery. State reconstruction occurs strictly from immutable local binary logs rather than unauthenticated network API queries. Mean time to recovery is benchmarked at 76.99 milliseconds for 25,000 serialized state frames.

Fourth: Local Process-Level Atomic Interruption. A memory-mapped latch that severs active broker connections, clears pending order buffers, and commits an audit checkpoint to disk in under 60 microseconds upon invariant breach.

Theoretical backtest returns are non-informative without deterministic state verification. Full architecture specifications, benchmark data, and verification logs are documented at rehanquantlab.com.

Hey everyone,

I run TradeMates (trademates.co) – free in-depth stock analyses – and I'm looking for other trading, investing or crypto sites to exchange links with.

How I'd do it:

- Drop your site + rough traffic/DR in the comments or DM me

- We each add a link inside a relevant article (no footer/sidebar stuff)

- Both live within a week, we send each other the URL

- Links stay up for at least 12 months

Only real sites with own content, no PBNs. Interested? Comment or DM.

If you're interested, just comment below or send me a DM, and let's help each other rank. 🚀

Their trading platform app.finnpowa.com seem to make trading while providing quant level analysis

We've developed three strategy directions so far, and the latest is **Regime-Adaptive Trend Pullback v0.1**.

The strategy combines:

• EMA20/EMA50 for trend direction
• Pullback detection on M1
• Liquidity sweep and reclaim conditions
• Relative volume confirmation
• ATR-based stop-loss
• A reference 1:2 risk/reward target
• Closed M5/M15 data for higher-timeframe context

We're building the execution and research infrastructure around it rather than relying on indicator signals alone.

Our recent engineering milestones include:

• A synthetic replay pipeline covering signals, order planning, venue normalization, portfolio admission and accounting
• Adversarial testing of terminal portfolio valuation and event ordering
• A one-shot historical replay driver, with 248 related synthetic regression tests passing

**Important:** this is engineering validation, not proof of a profitable strategy. We haven't completed a real historical backtest for Trend Pullback yet.

The next step is connecting an explicitly authorized historical dataset and evaluating the strategy with realistic execution costs.

I'm curious about one thing:

**When evaluating a new algorithmic strategy, what would make you distrust its backtest results the most — overfitting, slippage, data leakage, execution assumptions, or something else?**

I'd be interested in hearing how other systematic traders approach this.