Turning data into dollars with Python & ML ๐๐
San Francisco
Joined June 2022
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Alex Johnson retweeted
this AI trading battle just ended and the shorting results are so bad i can't believe nobody saw it coming
moon dev: "okay the real story is that they were terrible at shorting and the reason why is something i almost didn't say out loud"
"the season is over and every single number is sitting right there for anyone to grab"
"six AIs traded for me and then i handed over the entire dataset, and i still don't know if that was a smart move"
Alex Johnson retweeted
๐๐ฅ๐๐ ๐ง๐ฅ๐๐๐๐ก๐๐ฉ๐๐๐ช ๐ง๐ข๐ข๐ ๐๐ข๐ฅ ๐ฆ๐ข๐
Normalised Gaussian MACD Heikin Ashi
๐๐Introducing the Normalised Gaussian MACD Heikin Ashi by AlgoAlpha!
2.7k+ TradingView boosts. Open it free:
tradingview.com/script/9Ye6Bโฆ
Alex Johnson retweeted
Read this book if you want to learn trading lessons that have withstood the test of time
Alex Johnson retweeted
Python Library Spotlight: tabulate โ Smart Data Reports
Want to display Python data in clean, readable tables?
The tabulate library makes it easy to turn lists and other tabular data into beautifully formatted tables right in your terminal or notebook.
What can you do with tabulate?
Display data in structured tables
Customize table formats
Improve the readability of reports
Quickly identify top performers using Python's built-in functions
In this example, we display student names, skills, and scores in a formatted table and find the topper using max() and a lambda function.
Install the library:
pip install tabulate
Small Python libraries can make your code more readable and your reports more professional.
Explore more Python tutorials at clcoding.com/
Alex Johnson retweeted
10 GITHUB REPOS THAT GIVE AI X-RAY VISION FOR CODE.
1. Continue โ bring AI directly into your codebase.
Build coding assistants that understand project context and work inside your IDE.
github.com/continuedev/contiโฆ
2. Aider โ let AI navigate and modify your repository.
Work across multiple files, fix bugs and make code changes directly from your terminal.
github.com/Aider-AI/aider
3. Sourcegraph โ search massive codebases intelligently.
Trace functions, explore dependencies and find code across repositories.
github.com/sourcegraph/sourcโฆ
4. Serena โ give coding agents semantic code understanding.
Use language-server tools to navigate symbols, inspect references and edit code with context.
github.com/oraios/serena
5. Gitingest โ turn any Git repository into AI-readable text.
Convert a codebase into a structured text representation that is easier to feed into LLM workflows.
github.com/cyclotruc/gitiโฆ
6. Repomix โ pack entire repositories for AI analysis.
Bundle project files into a single AI-friendly output for code reviews, debugging and architecture questions.
github.com/yamadashy/repoโฆ
7. CodeQL โ uncover hidden bugs in source code.
Query code as structured data to detect security vulnerabilities and complex coding patterns.
github.com/github/codeql
8. Semgrep โ scan code for risky patterns.
Write custom rules to find bugs and enforce security practices across large projects.
github.com/semgrep/semgrep
9. Tree-sitter โ understand code beyond plain text.
Parse source files into syntax trees that power code navigation, analysis and developer tools.
github.com/tree-sitter/trโฆ
10. OpenGrok โ navigate huge codebases.
Search source code, browse cross-references and trace relationships between symbols.
github.com/oracle/opengrok
All are open-source.
Bookmark this. Understanding a million lines of code shouldn't require reading every single one.
Alex Johnson retweeted
๐๐ก๐๐๐๐๐ง๐ข๐ฅ ๐ฆ๐ฃ๐ข๐ง๐๐๐๐๐ง
Smart Money Volume Activity โ free on TradingView
This tool visualizes how Smart Money and Retail participants behave through lower-timeframe volume analysis.
Useful when you want to build consistency in their trading.
tradingview.com/script/POrDlโฆ
Alex Johnson retweeted
๐ Spot momentum shifts before price reacts!
The Volume Delta Oscillator with Divergence Signals by BigBeluga helps identify buying/selling pressure and potential reversals. ๐ฅ
FREE on #TradingView๐
go.bigbeluga.org/w84p0qxw3z
Alex Johnson retweeted
Why is energy conserved? Emmy Noether's 1918 answer: because the laws of physics do not change with time.
Her theorem pairs every continuous symmetry with a conserved quantity. Time-shift symmetry gives energy, space-shift symmetry gives momentum, and rotational symmetry gives angular momentum.
In Lagrangian form: if โโ/โq = 0, then d/dt (โโ/โqฬ) = 0.
Conservation laws are not separate rules. They are symmetry, seen from another side.
Alex Johnson retweeted
TRADING SETUP RULES FOR BEGINNERS.
If youโre new to trading, this is the cleanest way to build highโprobability setups without overcomplicating your chart.
Itโs all about aligning trend, momentum, structure, and confirmation.
BUY SETUP (Bullish)
โข Daily EMA trend aligned upward
โข H1 EMA alignment confirming direction
โข RSI oversold showing buyer interest
โข 50% retracement for a clean pullback
โข Bullish confirmation like engulfing or hammer
โข Support touch for entry confidence
โข Riskโreward minimum 1.5:1
SELL SETUP (Bearish)
โ Daily EMA trend pointing down
โ H1 EMA alignment confirming bearish flow
โ RSI overbought showing seller pressure
โ 50% retracement for a clean pullback
โ Bearish confirmation like engulfing or shooting star
โ Resistance touch for entry confidence
โ Riskโreward minimum 1.5:1
Simple rules. Clear structure. Beginnerโfriendly.
Alex Johnson retweeted
Trading hypothesis --> specific interaction --> structural replication --> returns linked to mechanism
Alex Johnson retweeted
How can one neuron slash its error by 75% with a single parameter tweak?
In this linear model, prediction is ลท = wx + b. Start with x=2, y=4, w=3, b=0: prediction is 6, squared loss is 2. Backpropagation via chain rule gives gradients โL/โw=4 and โL/โb=2. Learning rate 0.1 yields w=2.6, b=-0.2. New prediction is 5.0, loss drops to 0.5. This exact process, scaled to billions of connections, trains every modern neural network that recognizes faces, translates languages, or drives cars.
The smallest update can reshape an entire intelligent system.
Which real system do you think depends most on this error-driven learning?
Alex Johnson retweeted
๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฅ๐๐ ๐๐ถ๐๐ต ๐๐๐๐ฟ๐ฒ โ ๐ณ๐ฟ๐ผ๐บ ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐๐ ๐๐ผ ๐ฎ๐ป๐๐๐ฒ๐ฟ๐
Retrieval-Augmented Generation (RAG) isnโt magic.
Itโs a well-designed pipeline โ and Azure gives you all the building blocks.
๐ง๐ต๐ถ๐ ๐๐ถ๐๐๐ฎ๐น ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ๐ ๐ฅ๐๐ ๐ถ๐ป๐๐ผ ๐ฐ ๐ฐ๐น๐ฒ๐ฎ๐ฟ ๐๐๐ฎ๐ด๐ฒ๐ ๐
๐น Indexing
โข Parse documents (PDFs, docs, etc.)
โข Chunk text for better recall
โข Generate embeddings using Azure embedding models
โข Store them in a vector database
๐น Retrieval (R)
โข User query โ encoded into embeddings
โข Semantic search over the vector store
โข Fetch the most relevant chunks
๐น Augmentation (A)
โข Combine retrieved chunks
โข Build context-aware prompts
โข Ground the query with trusted data
๐น Generation (G)
โข Send prompt + context to the LLM
โข Generate accurate, hallucination-reduced responses
๐ก Key takeaway:
RAG is not about โasking the LLM harder questionsโ
itโs about feeding the LLM the right context at the right time.
If youโre building enterprise-grade GenAI on Azure, mastering this flow is non-negotiable.
๐ Save this for reference
๐ฌ Drop a comment if you want a deep dive on any stage
๐๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ฎ๐ ๐๐ ๐ถ๐ป ๐ท๐๐๐ ๐ญ ๐บ๐ถ๐ป๐๐๐ฒ ๐ฎ ๐ฑ๐ฎ๐. ๐๐ผ๐ถ๐ป ๐บ๐ ๐๐ฒ๐ฒ๐ธ๐น๐ ๐ป๐ฒ๐๐๐น๐ฒ๐๐๐ฒ๐ฟ ๐๐ต๐ฒ๐ฟ๐ฒ ๐ ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐๐ต๐ฒ ๐ฟ๐ฒ๐ฎ๐น-๐๐ผ๐ฟ๐น๐ฑ ๐ท๐ผ๐๐ฟ๐ป๐ฒ๐ ๐ผ๐ณ ๐๐ ๐๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป.
๐ ๐ฆ๐ถ๐ด๐ป ๐๐ฝ ๐ณ๐ฟ๐ฒ๐ฒ now โ avsl.beehiiv.com/
Follow @AiswaryaVenkit1 for more such insights!!
Alex Johnson retweeted
Compare accounts-receivable growth with sales growth, DSO, operating cash conversion, and historical collection behavior. The warning becomes when receivables expand while cash collected per dollar of revenue deteriorates.
Alex Johnson retweeted
Free eBook: Intro to Deep Learning โ Free Complete Book!
Author: Associate Professor, Bachelor of Computer Science and Engineering, Aditya University.
โข Neural Networks
โข Artificial Neural Networks
โข Deep Learning Fundamentals
โข Deep Learning Libraries
โข Deep Learning Architectures
โข Natural Language Processing
โข Recurrent Neural Networks (RNNs)
โข Sequence-to-Sequence Learning
โข Memory-Augmented Neural Networks
โข Deep Reinforcement Learning
โข Pattern Recognition
โข Advanced Deep Learning Techniques
A practical guide to understanding neural-network architectures, deep learning algorithms, and intelligent systems โ bridging fundamental concepts with applications in pattern recognition, language processing, and reinforcement learning.
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Free Complete Book:
geni.us/Intro-to-DeepLearn
Alex Johnson retweeted
WEโRE GOING LIVE!
October Python Bootcamp โ Day 8
Today, weโre diving into Data Structure Problem Solving in Python!
Knowing Python syntax is one thing. Choosing the right data structure to solve real-world problems is what makes you a better developer!
What weโll cover today:
โ
Choosing the Right Data Structure
โ
List vs Tuple vs Set vs Dictionary
โ
Nested Data Structures
โ
Frequency Counting
โ
Searching & Filtering
โ
Common Interview-Style Problems
Whether you're preparing for Python interviews or improving your problem-solving skills, this session is for you!
Join me LIVE on YouTube!
youtube.com/live/ErcjkW_4pgM
Alex Johnson retweeted
Iโve seen people spend months trying to understand how quants think.
It comes down to one thing.
They write a rule, test it on real data, and let the numbers decide.
probably thatโs the whole edge.
Alex Johnson retweeted
OPTIONโSELLING RISK, WHAT MOST TRADERS IGNORE UNTIL ITโS TOO LATE.
Selling call options looks easyโฆ until the market shows you how fast unlimited risk can hit.
โข Short Call in an Uptrend
When price keeps climbing, your sold call bleeds. Losses grow fast because upside is unlimited.
โข Short Call in a Downtrend
Even in a bearish market, one violent spike can erase weeks of profit and flip into extreme loss.
โข Risk Accumulation
Selling options feels safe at first, small gains, slow movement, until volatility explodes.
Option selling isnโt a game of prediction. Itโs a game of risk control, and ignoring that is how traders blow accounts.
Alex Johnson retweeted
WSX LATEST : POSCO International, South Koreaโs largest trading company with $22.2 billion in annual revenue, is bringing real-world trade receivables on-chain through a tokenization initiative built on @Injective in partnership with LG CNS.
The project aims to connect traditional trade finance with blockchain infrastructure by digitizing real commercial assets. Its pilot is already underway, with a gradual rollout into operational workflows planned in stages.
The move highlights growing interest in using blockchain to modernize trade finance and expand the adoption of real-world asset tokenization.
Alex Johnson retweeted
Machine Learning for Artificial Intelligence! #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
geni.us/ML-4-Books-AI
