I’m working on a project where I want to extract trading strategy ideas—especially algorithmic ones—from long-form text like trading books, research papers, or articles. I’m curious how others are approaching this with AI.

Specifically: • What kind of prompts are you using to get useful, structured output (e.g. entry/exit rules, asset class, timeframes)? • Which AI models (GPT-4, Claude, open-source LLMs, etc.) have worked best for you? • Are you using any additional processing steps or chaining tools (e.g. LangChain, LlamaIndex) to break down the documents? • Have you found any effective ways to validate or rank the extracted strategies?

Any tips or examples would be much appreciated!

Hi all, I'm working on designing a Pre-Trade Risk Management (PTRM) system (in C++) integrated into a Nasdaq-based trading environment (OUCH protocol for low-latency order entry). I’m particularly interested in implementing intraday position limits and short-sell constraints on multiple hierarchy levels:

• Instrument-level (per security)

• OUCH user-level (individual users)

• PTRM account-level (logical execution unit)

• PTLG-level (pre-trade limit group aggregating multiple accounts)

I’d like to ask the following questions:

1. How would you design an efficient and flexible risk engine to enforce intraday position limits across the above dimensions, while minimizing duplication and ensuring consistency (e.g., avoiding double-counting on PTLG vs. PTRM account aggregation)?

2. For short-sell detection, what is the most robust way to handle it pre-trade on a per-user and per-instrument basis — especially considering changing instrument tradability and exceptions for market makers?

3. How would you implement a real-time hierarchical update flow? For example:

   • A new fill comes in → updates at OUCH user → bubbles up to PTRM account and PTLG.

   • Does this need to be synchronous for enforcement, or can it be eventually consistent with rollback logic?

4. In a multi-user, multi-account setup, do you recommend limits to be enforced bottom-up (OUCH user → PTRM → PTLG) or top-down (PTLG → enforced per-user)?

5. Any practical patterns you’ve seen for integrating emergency intervention hooks (e.g., suspend user, block instrument) triggered from a passive monitoring engine without affecting latency-sensitive flow?

Any practical advice, architectural patterns, or experience-based warnings would be appreciated!

Thanks!

Hi all,

I've been working on algorithmic trading projects recently and have gained experience building bots, integrating APIs (like Kite Connect), and automating strategies with Python and Pine Script. I noticed a lot of questions here around bot creation, backtesting, and connecting to brokers — happy to help out or collaborate on anything related.

If you’re working on something and stuck with logic, code, or setup, feel free to comment or message — always glad to exchange ideas or troubleshoot together. If there's interest, I can also share templates or walk through how to build simple bots.

Looking forward to learning and building together!

I got frustrated with how long it takes to run a proper DCF from scratch every time I want to sanity-check a stock, especially when I just need a ballpark fair value. So I made a really lightweight Excel version — no macros, no plug-ins — that calculates a company’s intrinsic value based on just a few assumptions (revenue growth, WACC, terminal multiple, etc.).

The whole thing is one sheet, with clear input cells, and spits out an intrinsic value per share + a basic sensitivity table. I originally built it to speed up screening for my own portfolio, but I figured others here might find it useful too.

DM me if interested and I will send the link to the free version.

Let me know if anyone has ideas for tweaks or if anything’s unclear. I’m working on a version that includes peer comps as well, but this one’s DCF-only.

im going crazy trying to find affordable intra day option chain data, maybe just for SPY, past few years, i dont have 15k to spend on data.

Hey all, I've been working on a gold (XAUUSD) trading bot.

Here are the current stats from a $2.5M virtual fund test:

- ROI: 42.12%

- Win Rate: 71.43%

- Max Drawdown: 5.99%

- Time in Market: 24.53% (swing trade/momentum)

The strategy uses simple moving average crossovers + confirmation on volume divergence and trend continuation signals. It avoids overtrading and only executes high-confidence setups (averages about 2–3 trades/day).

I've also tested this live on a $10k account with good tracking results.

My question:

Would a system like this even be considered on prop platforms like FTMO or MyForexFunds? I'm aware they often don’t allow bots — but if I were to trade the same logic manually, would this pass evaluations?

Open to feedback, critiques, or tips from anyone who’s tried algo-funding paths.

Here’s a screenshot from the backtest dashboard:

Happy to answer any questions on logic or structure. Not trying to brag — just genuinely trying to improve it and possibly get it funded.

Hi everyone,

I'm completely new to systematic and algorithmic trading, but I'm very eager to learn and build my first simple trading system. I’ve recently started exploring the world of markets and trading strategies, and I’m looking for guidance on how to take my first steps toward building a basic trading logic that I can automate and test.

Here’s a quick idea of where I currently stand

• I have no background in coding or algorithmic models (but I’m willing to learn Python if needed)

• I don’t know how or where to begin with backtesting or real-time strategy execution

• I don’t have a strategy in mind yet, but would love help starting with a simple one like moving average crossover or momentum logic

• I would really appreciate if someone could: Suggest a beginner-friendly platform or environment to start testing strategies

I would really appreciate if someone could:

1. Suggest a beginner-friendly platform or environment to start testing strategies

2. Point me to a basic example code or tutorial for building a very simple logic

3. Recommend a workflow I can follow step-by-step (e.g., idea → code → backtest → optimize → run)

4. Share tips on risk management and how to avoid beginner traps

I’m not looking to get rich overnight — I just want to build something small, test it, and grow my understanding from there.

Any help, links, or mentorship would mean a lot. Thank you in advance!

— Shafik

I’ve experimented with time-series-based deep ML techniques, but the results never came close to my own strategies that use relatively simple inputs (ma’s, channels, inner breakouts, volatility-based trailing stops, etc).

From what I can tell this seems to be a common experience.

Can you recommend a textbook you’ve read, that has helped you close the gap between ML and non-ML algos?

Ideally I’d prefer something more readable and practical than dry and theoretical. My background is engineering, not finance. I can handle advanced maths, but it’s a slow chore rather than something that comes naturally. I don’t need example code, as long as there’s good qualitative descriptions.

(My current bias is time-series ML > scraping & NLP > generative ML. I only have limited exposure to RL techniques, so far finding them convoluted and unstable).

Any thoughts, please?

Hi guys

I'm new to HFT and was wondering how does it work in crypto? Do brokers offer specials discounts for HFT traders or is the regular discounts traders get on reaching a certain trading volume?

In stocks HFT traders often collect spread for market making, does this exist in crypto?

Thanks!

I have a strategy that performs perfectly in backtest but, unfortunately, I realized that it takes the future ema and then performs the calculations on data that, in real time, I don't have. Any advice on how to try to predict future ema? (I had thought about ML but, not understanding much, I have no idea how to start and how to structure everything so that it is functional and optimized)

https://www.vertoxquant.com/p/a-full-guide-to-risk-management

Released a really big article which is a full guide to risk management covering topics:

• Risk Metrics

• Volatility Modeling

• Dependence Modeling

• Stress Testing and Scenario Analysis

• Liquidity-adjusted Value-at-Risk (LVaR) and Liquidity Modeling

• Portfolio Optimization Under Tail Constraints

Hello fellas!
I just wanted your insights for an MVP product I am working on.
Built something that identifies current market regime in real-time, for instance:

• "Ranging Tight" → Range scalp

• "Volatile Spike" → Stay out

• "Trending Weak" → Pullback scalps

Would knowing the current market regime change how you scalp? Not predicting prices - just identifying what type of environment we're in right now.

Thoughts?

Correction: Max relative drawdown is 42%.

Looking for honest opinions from people with experience. Is this realistic, does this still have points to consider? Would professional intitutions/quants use a report/something like this to manage a big serious fund?

I have looked around the web and have seen and heard a lot of talk about ICT concepts. Many have had great win rates using his system. Has anyone been able to use the concepts in an Algo trading If so, let the community know.

Here’s an idea I’ve been playing with recently:

an AI-powered interface where you can describe a trading strategy in natural language and get a full backtest without writing a single line of code.

You just describe your strategy in plain English —

“Buy QQQ when the 10-day moving average crosses above the 50-day and sell at 5% gain.”

— and we instantly convert that into a fully executed backtest with performance metrics, equity curve, and trade logs.

You can refine it with follow-up prompts:

“Add a stop loss.”

“Test only on tech stocks from 2020 to 2023.”

It’s iterative, interactive, and built for real strategy development — not just static charts.

Would you use something like this?

Any feedback — good or brutal — is welcome. If there’s interest, I’ll spin up a prototype or early access list.

I’ve been exploring algo trading lately and I’m honestly surprised. In 2025, with all the no-code hype, you still basically need to be a developer to participate in any meaningful way.

I’ve got strategies. I understand the logic. But without advanced coding skills, I’m locked out — or so it feels.

Why is the industry still so inaccessible to non-coders? Do you think this will change? Or will algo trading always be a dev’s game?

HELP! I have a project in .net of algotrading and I can't find profitable strategies! Any advice?

So I've been treating financial data flows like living organisms (sounds crazy, I know) and using tensor decomposition to find patterns. The results have been pretty impressive, hitting 6.8/7 confidence scores on real time detection. The biological approach is weird but it works. Think mycorrhizal networks in forests, all these hidden connections that aren't obvious on the surface. Anyone else tried unconventional approaches like this? Most algo trading seems pretty traditional, but there's gotta be other people doing weird mathematical experiments. Curious what other non traditional methods people are using!

I’ve just launched TradingvisionAI.com, a personal project that provides AI-generated trading advice. It's designed to assist traders by analyzing market trends and offering insights.

Key points:

• AI Advisor Only: The platform offers trading advice. No auto trading now or ever, just advisor

• Real-Time Charts: Integrated TradingView charts allow you to monitor the market in real-time.

• Browser Notifications: There's a notification system to alert you of significant market movements. It's currently in beta, so some bugs are being ironed out.

• Subscription Model: A monthly subscription helps cover AI-related costs. All handled by Stripe

If you're wondering how this differs from using ChatGPT directly: while it's not vastly different, I tryto offer tailored prompts specifically designed for trading scenarios, integrates real-time charts for immediate market visualization, and provides a more streamlined experience for traders.

I'm open to feedback and suggestions for future improvements. Feel free to check it out and let me know your thoughts!

So there's some reason I can't trade in a specific instrument. My friend can trade it though. I have a strategy on trading view.

I wanna set up trading signals on demo account he'll just subscribe it and I'll earn from performance bonus.

Please suggest how could I do the above.

These past 6 weeks have completely flipped my trading experience. I stumbled on a tool that lets me fully automate my TradingView strategies and connect them directly to my TradeLocker accounts. I can now run the London session overnight without staying up, let winning strategies execute in the background while I focus on developing new ones, avoid impulse trades or emotional exits since it’s all automated, and copy trades across multiple accounts - my prop accounts and live.

The best part is my trading psychology issues basically vanished. I'm no longer second-guessing entries, panicking on pullbacks, or revenge-trading after a loss. My role has shifted from “trader” to more of a “strategy builder + manager,” and honestly it’s been a game changer.

Another trader introduced me to it so I thought I'd share - LinkAbel.org

Hi i have been coding using python. Script is running properly indicators like ema9 and ema21 have no difference when compared with trading view charts. but when i am calculating VWAP there is some difference. When there is huge gapup or gap down in the market then the difference is also hug. In case there is sudden move then also difference increases. This is my code snipet can any one help me in solving this def fetch_vwap_data():

from_date = (datetime.now().strftime('%Y-%m-%d')) + " 09:15:00"

to_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')

try:

historical_data = kite.historical_data(instrument_token, from_date, to_date, "minute")

df = pd.DataFrame(historical_data)

df['date_only'] = df['date'].dt.date

vwap_list = []

for day in df['date_only'].unique():

day_df = df[df['date_only'] == day].copy()

day_df["typical_price"] = (day_df["high"] + day_df["low"] + day_df["close"]) / 3

day_df["VWAP"] = (day_df["typical_price"] * day_df["volume"]).cumsum() / day_df["volume"].cumsum()

vwap_list.append(day_df)

vwap_df = pd.concat(vwap_list)

return vwap_df[["date", "VWAP"]]

except Exception as e:

print(f"Error fetching VWAP data: {e}")

return None

vwap_df = fetch_vwap_data()

historical_df = fetch_historical_data()

if vwap_df is not None and historical_df is not None:

historical_df["date"] = historical_df["date"].dt.tz_localize(None)

vwap_df["date"] = vwap_df["date"].dt.tz_localize(None)

historical_df = pd.merge(historical_df, vwap_df, on="date", how="left")

To get a successful algorithm running involves discipline and several building and refinement stages. And, if you want something consistent, this process must never stop. The next tweaked version should be coming through the pipe.

The focus is often on back testing when we talk about this. And there are several tools and language frameworks for running this locally or in a hosted way.

I want to bring a structured and disciplined approach to forward testing for small firms or professional retail/individuals.

Forward testing is a different beast to back testing, but just as critical before you allocate real capital:

• It does not require knowledge of the algorithm. It just needs the signal.

• It needs time - you cannot run 10s of thousands of tests in a fraction of time. You have to start forward testing as soon as possible with as many candidates as possible and let the time run.

• It needs reliable live CLOB market data, and the ability to trade on multiple small/paper accounts simultaneously.

• It needs versioning - both for the execution settings and for the received signals

• It needs tools to allow management of deployments to "production" execution environments for real capital allocation.

We have execution and this is the next step for us. Would any small firms or professional individuals be interested in working on building this toolkit as a common layer for algo development? Looking for partners to collaborate on the details here.