📈 JP Morgan and top quant firms are using computer vision to trade stocks. Why aren’t you?
Wall Street isn't just looking at candlesticks anymore — it's scanning time series as images.
Getting more and more tradable systems designed by LLMs.
This is what I’ve learned actually works for me.
👇 Step-by-step breakdown of the process that turns AI ideas into real systems I can paper trade.
Market Makers don't manipulate price—
we're trapped by our own hedging requirements.
When SPX drifts between long and short strikes, our systems start buying and selling futures in ways that create predictable paths.
(short thread)
In my 20 years of trading I have noticed this cycle play out again and again with traders that "make it":
1. Overconfidently reach for returns
2. Get humbled by the market
3. Simplify + concentrate on clear, high probability edges.
1/n
Sun May 18
CODE GIVEAWAY
In an effort to accelerate your implementation of a very decent trailing stop and a great momentum indicator ... I'm sharing mine with you.
First:
1) I can't take time to teach you how to install a Thinkscript (I'll tell you the basic part, below) ...…
Options move markets.
Market makers hedge positions.
Dynamically...
Systematically...
they have no choice.
it isn't random.
and it moves markets.
Why it Matters: You can better predict the market's behavior when you know the positions and understand how hedging works.
Darkpool Volume Sentiment heatmap as of May 2 close.
I think there are some great long/short pair trades in the megacaps.
$META $NVDA $AVGO $AMZN AI stonks are showing much more positive relative volumes compared to $AAPL and $TSLA (China exposure) and laggard $GOOGL
$BRK.B…
Could a simple intraday momentum strategy really beat the market?
I backtested the concept from this SSRN paper (originally for SPY) on NQ mini futures using TradeStation: papers.ssrn.com/sol3/papers.cf…
The core idea is surprisingly straightforward:
1️⃣ Define a “Noise Area” based on…
According to Ray Dalio, the easiest way to adjust for risk is to seek uncorrelated returns.
Ray's made billions from a simple idea.
Here's how to do it in a few lines of Python code:
Machine learning isn’t the FUTURE of trading...
Because It’s already here – Forecasting trends to clustering market patterns.
Here are some free ML-based indicators you need in your toolkit.
The last one is a game-changing! 🧵
As a follow-up to #ETF data, get this, courtesy @SPGlobal: The ONLY net buyer of US equities are indexes and ETFs. Everyone else is a seller. And this is why multiples keep rising. But it presents the risk of a massive reset WHEN indexes/ETFs stop buying equities.
"Expected Option Returns and Large Language Models": "I employ Large Language Models (LLMs), including BERT and an OpenAI model, to extract information from news articles and predict option returns. LLM-based news portfolios achieve annualized Sharpe ratios of up to 3.15 and…
"Beyond Mean-Variance Optimization: Finding Optimal Portfolios with Hierarchical Risk Parity”-A hands-on tutorial on how to do it in Python with skfolio
medium.datadriveninvestor.com/beyond-mean-va…
📢 New Investment Ideas!: "Does sentiment help in asset pricing?
A novel approach using large language models and market-based labels"
📈 Keep reading!🔻
👉 Researchers have developed a novel approach to sentiment analysis in financial markets using a state-of-the-art large…
QQQ: rolling gex is still growing, however, price is breaking upper bollinger band and RSI is breaking 70. Taking profit is recommended. Repost if you like my charts.
New Research Ideas! "Empirical Asset Pricing with Probability Forecasts"
Instead of predicting returns, this paper introduces an intriguing approach by predicting the probabilities that stocks will outperform a benchmark.
Here are my main takeaways from the paper: 👇
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