KYUNGJUN LIM’s Post

Stat Arb Automation: Why Python Isn’t Enough. ⚡ Everybody talks about statistical arbitrage as a math problem. But in High-Frequency Trading (HFT), it is primarily an engineering bottleneck. You can find the most beautiful co-integrated pair on Earth, but if your tick-to-trade latency is above 1ms, your alpha will decay before you even hit the exchange. Here is the micro-architecture I use to keep execution ultra-fast, combining Python’s flexibility with C++'s bare-metal performance: 1️⃣ WebSocket Stream: Direct market data ingestion into Redis (in-memory) for zero I/O overhead. 2️⃣ Strategy Engine (Python): Subscribes to Redis, calculates the moving average, standard deviation, and the real-time Z-Score of the spread. 3️⃣ Low-Latency Messaging (ZeroMQ): The second the threshold (e.g., Z < -2.1) is hit, ZeroMQ pushes a message to the execution engine. 4️⃣ Execution Gateway (C++): A multi-threaded, bare-metal C++ application that generates and executes the actual FIX order (NSE/HFT API) in microseconds. No GIL, just performance. The visualization chart on the right shows the exact moment the z-score triggers an automated “AUTO-BUY SPREAD” trade. Stop guessing the market direction. Start engineering the execution. 🛠️ Question for the developers: Why are you still using Python for your execution gateway? Let me know below! 👇 #QuantDev #SystemDesign #SoftwareArchitecture #HighFrequencyTrading #Redis #ZeroMQ #LowLatency #StatisticalArbitrage #HFT #Python #AlgoTrading

While working through the qualifying rounds of IMC Prosperity this year, I kept running into the order book as a core piece of the simulation. I understood it conceptually but had never actually built one. So I did. I wrote a post walking through 4 iterations of a limit order book in Python, starting from a naive price-to-quantity mapping and working up to a concurrency-safe implementation. A few things I found interesting along the way: • The naive version looks fine until you try to cancel a specific order and realise you have no way to do it • Price-time priority (FIFO) completely disappears when you merge orders at the same level into one number • Adding a lock is easy. Adding a lock correctly, without deadlocking or killing throughput, is not • Figuring out the trade price is harder than it looks, especially once you introduce concurrency into the picture Full post and code on my site: https://lnkd.in/eBHgvQ9T #Python #Trading #Algorithms #TUDelft

Here’s a simple Python roadmap to follow: 🔹 Step 1: Basics Build your foundation → Syntax, variables, data types → Conditionals, functions, exceptions → Lists, tuples, dictionaries 🔹 Step 2: Object-Oriented Programming Think like a developer → Classes & objects → Inheritance → Methods 🔹 Step 3: Data Structures & Algorithms Level up problem-solving → Arrays, stacks, queues → Trees, recursion, sorting 🔹 Step 4: Choose Your Path This is where things get interesting → Web Development Django, Flask, FastAPI → Data Science / AI NumPy, Pandas, Scikit-learn, TensorFlow → Automation Web scraping, scripting, task automation 🔹 Step 5: Advanced Concepts → Generators, decorators, regex → Iterators, lambda functions 🔹 Step 6: Tools & Ecosystem → pip, conda, PyPI

Mask First, Apply Later In Pandas Many data professionals use .apply() everywhere because it feels intuitive. However, I noticed some of them using .apply() like looping through rows in Python(similar to iterrows in padas), which becomes slow on large datasets. It is not the right/wrong approach, but it is just a matter of speed. A better approach: 👉Use masking/filtering first 👉Then apply logic only to relevant rows To make a comparison, let's look at a simple operation, where you have 1M of personal age and income, and you want to create a column to store a bonus of 2% for people over 60. Without masking(Method1), the task will take approximately 4.82 secs, while masking and then .apply() (Method2) only took 0.03 secs, or 160 times faster. Why is masking faster? Row-wise .apply(): - Executes Python code row by row - Time complexity ≈ O(n) Masking + vectorized operations: - Uses optimized NumPy operations underneath - Runs in compiled C code My Personal Approach, when dealing with a large dataset:  Before using .apply(), I always ask myself: “Can this be done with vectorized operations or masking?”

Here’s a simple Python roadmap to follow: 🔹 Step 1: Basics Build your foundation → Syntax, variables, data types → Conditionals, functions, exceptions → Lists, tuples, dictionaries 🔹 Step 2: Object-Oriented Programming Think like a developer → Classes & objects → Inheritance → Methods 🔹 Step 3: Data Structures & Algorithms Level up problem-solving → Arrays, stacks, queues → Trees, recursion, sorting 🔹 Step 4: Choose Your Path This is where things get interesting → Web Development Django, Flask, FastAPI → Data Science / AI NumPy, Pandas, Scikit-learn, TensorFlow → Automation Web scraping, scripting, task automation 🔹 Step 5: Advanced Concepts → Generators, decorators, regex → Iterators, lambda functions 🔹 Step 6: Tools & Ecosystem → pip, conda, PyPI 💡 The truth? Python isn’t hard—lack of direction is.

A robust understanding of FastAPI’s Dependency Injection (DI) system is critical for designing scalable Python backends.  While Depends() seems simple, the underlying mechanic is a highly  optimized engine processing complex architectural constraints. Here is a  theoretical breakdown of how FastAPI resolves dependencies. 👇  1️⃣ Inversion of Control via Marker Objects Depends() operates purely  as a frozen dataclass marker. Assigning it to a parameter engages  Inversion of Control (IoC), delegating component instantiation and  lifecycle management directly to the framework.  2️⃣ Phase 1: Startup Introspection & Graph Construction FastAPI  decouples dependency analysis from execution. During application boot,  it initiates introspection using Python's inspect module. It recursively  maps endpoint requirements to build a static execution graph, the  "Dependant Tree." Expensive reflection operations occur exactly once.  3️⃣ Phase 2: Runtime Resolution & Thread Boundaries Upon receiving a  request, the framework traverses the Dependant Tree with minimal  overhead, making context-aware decisions: • State: Dependency results  are deterministically cached per-request to ensure transaction  consistency. • Concurrency: I/O-bound async dependencies execute on the  primary event loop. • Thread Isolation: Blocking, synchronous parameters  are automatically offloaded to an external thread pool, preventing  event loop saturation.  4️⃣ Structurally Guaranteed Teardowns When managing stateful resources  (e.g., database connections), dependencies utilizing Python generators  (yield) are wrapped in an AsyncExitStack. This framework-level context  manager executes instantiation, yields control to the endpoint, and  structurally enforces teardown after the HTTP response transmits,  comprehensively mitigating human-error resource leaks.  5️⃣ Architectural Decoupling for Testing Because FastAPI exclusively  owns the resolution graph, testing shifts from brittle string-based  mocking (mock.patch()) to true interface substitution. By injecting  overrides directly into dependency_overrides, engineers can seamlessly  substitute deep-level production components with mocks. The dependency  graph adapts dynamically, ensuring resilience against refactoring.   Master the internal mechanics of your framework to construct truly resilient systems.  #Python #FastAPI #BackendEngineering #SoftwareArchitecture #DependencyInjection 

Handling complexity in long running Python services often feels like juggling fragile glue code, retry loops, watchdogs, and scattered flags. Di Lu’s article, “A supervisor tree library for building predictable and resilient programs,” offers a compelling approach with Runsmith, a Python library inspired by Erlang/OTP supervisor trees that models each unit as a typed worker with an explicit lifecycle. You can read the full breakdown here: https://lnkd.in/dgxjFnpx. What stands out is the shift from brittle process level restarts to fine grained fault isolation and health monitoring that catches stalls and constraint violations, not just crashes. This aligns with challenges I’ve faced building multi component platforms where uptime matters and failure domains must be confined. One caveat is that adopting such a framework requires upfront discipline in designing worker lifecycles and state machines, which can add complexity early on. However, this investment pays dividends when shipping real products that demand maintainability and predictable fault recovery. How have others balanced this upfront design effort against the operational resilience gains in production? #python #softwarearchitecture #systemdesign #reliabilityengineering #productdevelopment #founders #engineering #faulttolerance #opensource #devtools #resilience #longrunningservices

UNLEASHED THE PYTHON!i 1.5 ,2, & three!!! 14 of 14(B of B) copy & paste Ai Headline: Revolutionizing Data Streams with the 'Cyclic41' Hybrid Engine Libcyclic41. *A library that offers the best of both worlds—Geometric Growth for expansion and Modular Arithmetic for stability. Most data growth algorithms eventually spiral into unmanageable numbers. I wanted to build a library that offers the best of both worlds—Geometric Growth for expansion and Modular Arithmetic for stability. The Math Behind the Engine: Using a base of 123 and a modular anchor of 41, the engine scales data through ratios of 1.5, 2, and 3. What makes it unique is its "Predictive Reset"—the sequence automatically and precisely wraps around at 1,681 (41^), ensuring system never overflows. Key Technical Highlights: Ease of Use: A Python API wrapper for rapid integration into any pipeline. Raw Speed: A header-only C++ core designed for millions of operations per second. Zero-Drift Precision: Integrated a 4.862 stabilizer to maintain bit-level accuracy across 10M+ iterations. Whether you're working on dynamic encryption keys, real-time data indexing, or predictive modeling, libcyclic41 provides a self-sustaining mathematical loop that is both collision-resistant and incredibly efficient. 🚀 Get Started with libcyclic41 in seconds! For those who want to test the 123/41 loop in their own projects, here is the basic implementation: 1️⃣ Install the library: pip install cyclic41 (or clone the C++ header from the repo below!) 2️⃣ Initialize & Grow: | V python from cyclic41 import CyclicEngine # Seed with the base 123 engine = CyclicEngine(seed=123) # Grow the stream by the 1.5 ratio # The engine handles the 1,681 reset automatically val = engine.grow(1.5) # Extract your stabilized sync key key = engine.get_key() /\ || Your Final Project Checklist: * The Math: Verified 100% across all ratios (1.5, 2, 3). * The Logic: Stable through 10M+ iterations. * The Visuals: Infinity-loop diagram ready for the main post. * The Code: Hybrid Python/C++ structure is developer-ready. 14 of 14(B of B) Not theend NOT THEE END NOT THE END

How fast is your "fast" model when pushed to the limit? It is not just about whether an LLM can find the information, but how quickly it can start delivering it. NEO built Context Cost Map : A Python tool that maps accuracy, cost, and latency. By precisely tracking the "time to first token" across varying context sizes, the Context Cost Map tool exposes the real-world speed of models under pressure 5 models tested across 9 context sizes (1K-64K) with 3 trials each (135 API calls total). How is this measured? Context Cost Map runs a rigorous "Needle-in-Haystack" evaluation. The tool dynamically generates filler text to reach target sizes from 1K up to 128K tokens, hides a secret target fact "DELTA-7", and forces the LLM to retrieve it. The Context Cost Map orchestrates API calls via OpenRouter. It automatically tracks binary accuracy, latency, and USD cost, instantly generating interactive HTML subplots to visualize performance inflection points. Context Cost Map tool is fully open-source and ready for your own custom model evaluations Map the precise intersection of cost, latency, and accuracy for your production stack today.