For any of you who have been down this road - for your database and your historical IV and greeks for options, what time do you take the data from?

I’m completely new to algorithmic trading, so I decided to spend the past few days developing a strategy for learning purposes to see how it would play out, and have been pleasantly surprised by the results after running a lot of backtesting over multiple time frames after factoring in commissions and slippage. My question now is how would I be able to apply this strategy to an automated trading bot? Ideally, to trade on a 50-150K account through a futures prop firm such as TopStep? (This strategy is specialized for trading MES1! and MNQ1! tickers) Any help would be appreciated.

If you are making these amounts of profit without Sharpe ratios this high, then you are overleveraged. The volatility numbers are just to illustrate how much leverage the Kelly criterion would recommend. They do not impact the expected returns.

What are signs of a resilient strategy? Specific examples? E.g., How should it perform during outlier, anomalous, highly volatile periods?

How should it perform during market conditions in which it shows weakness? E.g., it shows weakness during flat and ranging conditions, as opposed to trending. What specific stats, drawdowns, metrics, etc., are acceptable? 30% drawdown during suboptimal conditions?

Hello all, I had no idea this group existed and also had no idea "algorithmic trading" was what I'd been doing for years so thanks for allowing me to join!!!

After reading through all the different posts I can't stop from wondering by so many people "fail" at the algo approach and if the reasoning behind the perceived failure is a lack of patience, or is in fact the algorithm. Don't get me wrong, I know this isn't for everyone nor is it easy, but I'd guess 99% of the people who go down this route have the basic fundamentals to build a modestly successful algorithm. Modestly successful is where I'm guessing most people give up, especially if the initial capital people can invest is low?

Hey all,

I’m an experienced Python developer currently working with end-of-day (EOD) options data for analysis and charting. I’ve been pulling data via the Schwab API and IBKR, building out a workflow that helps track trends, volume shifts, open interest moves, and other potential signals.

Just wondering if there are others here doing similar work?

What data sources or brokers are you using? Are you building everything in Python, or using Google Sheets, dashboards, or other tools in your pipeline? I’m particularly interested in how people structure their analysis and what metrics they focus on—unusual activity, IV shifts, or even just systematic scans.

Would be great to exchange ideas, workflows, or even some automation tricks.

Looking forward to connecting with others in this space.

Thank You

Hi r/algotrading — I've developed an open-source stock screener that integrates traditional financial metrics with AI-generated analysis and news sentiment. It's still in its early stages, and I'm sharing it here to seek honest feedback from individuals who've built or used sophisticated trading systems.

GitHub: https://github.com/ba1int/stock_screener

What It Does

• Screens stocks using reliable Yahoo Finance data.
• Analyzes recent news sentiment using NewsAPI.
• Generates summary reports using OpenAI's GPT model.
• Outputs structured reports containing metrics, technicals, and risk.
• Employs a modular architecture, allowing each component to run independently.

Sample Output

json { "AAPL": { "score": 8.0, "metrics": { "market_cap": "2.85T", "pe_ratio": 27.45, "volume": 78521400, "relative_volume": 1.2, "beta": 1.21 }, "technical_indicators": { "rsi_14": 65.2, "macd": "bullish", "ma_50_200": "above" } }, "OCGN": { "score": 9.0, "metrics": { "market_cap": "245.2M", "pe_ratio": null, "volume": 1245600, "relative_volume": 2.4, "beta": 2.85 }, "technical_indicators": { "rsi_14": 72.1, "macd": "neutral", "ma_50_200": "crossing" } } }

Example GPT-Generated Report

```markdown

AAPL Analysis Report - 2025-04-05

• Quantitative Score: 8.0/10
• News Sentiment: Positive (0.82)
• Trading Volume: Above 20-day average (+20%)

Summary:

Institutional buying pressure is detected, bullish options activity is observed, and price action suggests potential accumulation. Resistance levels are $182.5 and $185.2, while support levels are $178.3 and $176.8.

Risk Metrics:

• Beta: 1.21
• 20-day volatility: 18.5%
• Implied volatility: 22.3%

```

Current Screening Criteria:

• Volume > 100k
• Market capitalization filters (excluding microcaps)
• Relative volume thresholds
• Basic technical indicators (RSI, MACD, MA crossover)
• News sentiment score (optional)
• Volatility range filters

How to Run It:

bash git clone [https://github.com/ba1int/stock_screener.git](https://github.com/ba1int/stock_screener.git) cd stock_screener python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows pip install -r requirements.txt

Add your API keys to a .env file:

bash OPENAI_API_KEY=your_key NEWS_API_KEY=your_key

Then run:

bash python run_specific_component.py --screen # Run the stock screener python run_specific_component.py --news # Fetch and analyze news python run_specific_component.py --analyze # Generate AI-based reports

Tech Stack:

• Python 3.8+
• Yahoo Finance API (yfinance)
• NewsAPI
• OpenAI (for GPT summaries)
• pandas, numpy
• pytest (for unit testing)

Feedback Areas:

I'm particularly interested in critiques or suggestions on the following:

1. Screening indicators: What are the missing components?
2. Scoring methodology: Is it overly simplistic?
3. Risk modeling: How can we make this more robust?
4. Use of GPT: Is it helpful or unnecessary complexity?
5. Data sources: Are there any better alternatives to the data I'm currently using?

I'm a lifelong coder by trade but I've spent the last few months putting my ai knowledge into developing a forex strategy which has proven surprisingly robust in backtest.
I've built a great deal of risk management into the system and factored in conservative rates for slippage, fees, trade delays, etc. I've backtested several years of data and been paper trading w/ live data for the last couple of months.
My question is - what am I missing, or rather, what things did you guys miss when you started running your first strategy? What are some common novice mistakes or blind spots?
Thanks for any advice you can offer...

Just wanted to ask if anyone else was also doing the IMC trading challenge either now or has done in the past.

Hello everybody,
I am trying to understand the workflows of a professional equity researcher. I would love to talk to you if you are involved in any kind of equity research, mostly on fundamental side: Reading earnings transcripts, SEC filings and so on as a profession.

Thank you

[Not promoting, Not selling, Just sharing the news. Feel free to remove if violates] I have published "Tariffs Radar" - April 4 Midday news on the subject: https://www.reddit.com/r/EverHint/comments/1jrkxal/news_and_sentiment_in_a_nutshell_april_4_2025/

The reality is rather stormy these days, so be informed, do not panic, we will go thru this. Just sit tight and observe what's going on.

Peace

Any recs for a cheap live extended hours data provider? I don't need anything other than live data and needs to cover extended hours. Polygon/databento are $200 monthly, alpaca is $100. I use live data infrequently and would prefer to cut this cost. Thanks.

I want to test a few ideas I have, but I'm not sure if there any free sources for SP500/nasdaq daily prices and bond yields? I use python or R, so libraries for those could work. IIRC yahoo finance is not working anymore?

I have developed a strategy in TradingView using the 1day/4-hour timeframe.

I noticed that the daily candle in IBKR starts at 12:00 AM, whereas in TradingView, it starts at the exchange time — 6 PM CST.

Is there a way to adjust the candle start time in the settings? I know I can manually reconstruct 4-hour candles from 1-hour data in code, but I'm hoping there's a quicker or built-in solution.

Edit: After checking, even the opening price on the 1-hour timeframe is different? I'm subscribed to real-time futures data across all exchanges, and this is the result I’m seeing?

Can anyone please help me by telling what is the nomenclature followed by kite api by zerodha for nifty weekly expiry option contracts ?

prove me wrong and you win the debate. simple.

I have written my new algotrading algorithm and am running it on Alpaca, but I have to re-evaluate every 3 days due to pattern day trader restrictions on margin accounts (which makes sense). Whilst I am making good returns my algorithm works best (when back tested) on pockets of change

I’m not willing to put more than $5,000 into it at the moment, but I am aware the equity requirement is $25,000 as it is a margin account. I don’t need the margin, but I would like the trading frequency. I haven’t had this issue on the European broker market, so Any good platforms for this I should look into?

Hey everyone,

I'm looking for an algo trading platform that allows running indicator-based strategies on combined premium charts specifically for Indian markets. My main requirements are:

• Ability to create and deploy custom strategies using indicators
• Support for combined charts (e.g., ratio charts, spread charts, or custom chart overlays)
• Integration with Indian brokers or exchanges (NSE, BSE, MCX)
• Reliable execution and backtesting features

Does anyone here have experience with a platform that fits these needs? Would love to hear about your experiences and recommendations!

Thanks in advance!

Hey guys,

We’re a startup launching a USD ↔ USDC P2P marketplace in a couple weeks. Several million in buy-side demand is committed, but we have very few sellers—so early USDC sellers will see some large arb spreads.

If you’ve done any crypto/stablecoin arbitrage, I’d love to get your advice:

• What helps you trust a new platform?
• What are the biggest risk red flags?
• What early data (spread info, liquidity metrics, etc.) do you need to see to consider a platform to be a good candidate for arbitrage?

We’re backed by a16z and HF0, but still early—we want to get this right for people who actually trade so we can get some liquidity flowing through the platform.

Thanks in advance for your help!

I have optimized roughly 15 scalping strategies on the past 20 days worth of data for a stock, The backtesting is on those same days and I have selected the best performer. Obviously I can’t expect it to perform the same as the backtesting on the next week but should I expect it to fail altogether? Would a better approach be to save the last 5 days for backtesting and optimize on the 20 days prior to those? How do you guys separate your data for optimization and testing? What other approaches are there?

Edit: using 1-min data

Relatively new to forex, coder by trade.
I have a strategy that is working quite well in backtest and I'd like a low latency sandbox to trade in with wide market coverage. What is the go-to solution for this? My understand is that MetaTrader 5 is best.
Thanks for any advice.

Do you keep it running or pause it during news?

Decided to trust my model yesterday during the tariff news, was worth it and avoided the big drop.

I usually don't like news times and pause my algo, but I kept it this time. Honestly I felt more like gambling than anything else, I knew it was going to hit TP or SL during speech , but no one know which one!

What's the best way to handle news times?

How have you modified your Alpha-generation strategies to capitalize on the sector rotations we're seeing in today's market environment?