FinLang 0.7.4 Released on PyPI with Exclude Feature

🚀 FlameIQ v1.0.2 Released FlameIQ is an open-source performance regression detection engine designed for CI environments. The tool compares benchmark results against a stored baseline on every CI run and detects regressions using configurable thresholds and optional statistical testing. Key capabilities • Compares benchmark results against a stored baseline on every CI run • Enforces per-metric thresholds with direction-aware regression logic • Optional Mann–Whitney U statistical significance testing • Generates self-contained HTML performance reports • Outputs machine-readable JSON results for CI pipelines Installation pip install flameiq-core Resources Documentation: https://lnkd.in/d6e2D7mq PyPI: https://lnkd.in/d-2KcKFd Source Code: https://lnkd.in/d2VDWRQa Contributions and feedback are welcome as the project continues to evolve. #opensource #performanceengineering #python #devtools #cicd

Are you optimizing your HFT stack for nanosecond precision? In the world of High-Frequency Trading (HFT), milliseconds are the difference between profit and loss. While many traders rely on standard REST APIs, the real edge lies in mastering low-latency protocols. My latest article dives deep into: 🔹 FIX over WebSockets: Why this hybrid approach is a game-changer for speed. 🔹 PTP (Precision Time Protocol): Going beyond NTP to achieve sub-microsecond synchronization. 🔹 Python Implementation: Practical steps to build a high-performance trading bridge. Whether you're a Quant Developer or an Algorithmic Trader, understanding the synergy between WebSocket efficiency and PTP accuracy is crucial for modern market execution. Link in the comments for the detailed guide #HFT #AlgorithmicTrading #Python #FinTech #QuantFinance #WebSockets #LowLatency

Claude can write a compiler, but it can't spell "anthropic" backwards. I tested it: pure LLM: "cipohtrpna" => wrong. Give it a sandbox? It writes `echo "anthropic" | rev`, runs it, gets "ciporhtna." => correct 10 tests where LLMs fail: counting, reversal, prime factorization, set ops. No sandbox: 8/10. With sandbox: 10/10. I didn't tell it to write code, it just recognized what it's bad at and reached for Python! Anthropic just shipped Programmatic Tool Calling (Claude writes code that calls your tools). Fewer round-trips, less tokens. It runs in their container, not yours. Testing it, something occurred to me: does that still work without tools? Yes, and it patches its own blind spots with code! Have you tried PTC already? #AICoding #ClaudeCode #AIAgents #LLM

✅ Day 8 of #DSAPrep > Problem: Convert Number to Hexadecimal > Platform: LeetCode > Concept: Base Conversion (Decimal → Hexadecimal) Converted an integer to its hexadecimal representation without using built-in conversion functions. > Key points: - Used modulo 16 to extract digits - Mapped values 10–15 to 'a'–'f' - Handled negative numbers using 32-bit two’s complement logic - Reversed the result at the end > Time Complexity: O(log₁₆ n) > Space Complexity: O(log₁₆ n) Example: 28 → 1c #DSAPrep #Algorithms #Python #NumberSystem #ProblemSolving #CodingJourney

I’ve been implementing asyncio to handle my project API request layer more efficiently. By moving away from sequential execution, the pipeline now manages multiple I/O operations concurrently, virtually eliminating idle time during network waits. With this implementation, the pipeline reaches a higher throughput and a much more resilient data collection process. Check out the benchmark below comparing synchronous vs. asynchronous execution. The difference in performance speaks for itself. #Python #AsyncIO #DataEngineering #AnalyticsEngineering #QftB #WebScraping #Performance

🚀 Day 35/60 — LeetCode Discipline Problem Solved: Remove Nth Node From End of List Difficulty: Medium Today’s challenge was about linked lists and efficient traversal. Instead of calculating the length first, I used the two-pointer approach to remove the target node in a single pass. 💡 Focus Areas: • Two-pointer technique (fast & slow pointers) • Linked list traversal optimization • Handling edge cases (removing head node) • Use of dummy node for cleaner logic • Writing efficient O(n) solutions ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) Two pointers… one moving ahead, one following behind— like time and memory walking together. And at the right moment… one node quietly disappears, as if it was never meant to stay. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #LinkedList #TwoPointers #CodingJourney #ProblemSolving #Python #Developers #TechGrowth #Consistency

When an LLM stops guessing and starts coding, reasoning transforms. The 2023 ‘Chain of Code’ paper revealed a powerful truth: for precise tasks, LLMs should write scripts handing logic to a Python interpreter and only stepping in when simulation fails. At Neksogix, we see this as the shift from monologue to orchestration, LLMs as managers delegating execution for verifiable automation. How is your organization leveraging code-generation in agentic workflows?  #Neksogix #ChainOfCode #AgenticAI #LLMs

MLForge Launches Visual Pipeline Editor for PyTorch Model Development 📌 MLForge just dropped a visual editor that lets you build PyTorch models without writing a single line of code-drag and drop nodes to define data pipelines, architectures, and training loops, then export clean Python scripts. Ideal for fast prototyping while keeping full control over model logic, it bridges the gap between simplicity and precision in ML development. 🔗 Read more: https://lnkd.in/dqz2mcg3 #Mlforge #Pytorch #Visualpipeline #Nodebased #Dragdrop

Most backtests are quietly lying to you - and look-ahead bias is why. Built a vectorized SMA crossover backtesting engine in Python that makes results much closer to real trading outcomes. Every crossover signal is shifted by one day, so a signal on day N only enters on day N+1, no trades based on information from the future. The engine uses pandas.rolling() with boolean masking instead of row-by-row iteration, keeping signal generation at O(n) complexity. On AAPL from 2020 to 2024, a simple SMA 20/50 strategy returned 69.08% vs. 180% buy-and-hold, with a Sharpe of ~0.45 and a max drawdown of ~-18%. Most of this came together during long flights to and from South Africa, which turned into surprisingly productive coding time at 35,000 feet. The architecture is split into four modules: data_loader, strategy, backtest, and visualizer, with yfinance as the data layer and CSV caching to cut redundant API calls. The goal is a modular setup where strategies can be swapped without touching the backtesting core. Repo is open source: https://lnkd.in/dgavp8DG #Quant #AlgoTrading #Python #Backtesting #QuantFinance #OpenSource

60 Days of Problem Solving Challenge - Day 25 👉 Today’s Problem: Generate IP Addresses The task was to generate all possible valid IP addresses from a string of digits. A valid IP must have four segments (A.B.C.D), where each segment ranges from 0 - 255 and cannot have leading zeros unless the segment itself is 0. 💡 Approach / Logic: Used a Backtracking (DFS) approach. Try forming segments of length 1 to 3 digits. Validate each segment: ✔ Value must be between 0 and 255 ✔ Avoid leading zeros like "01" or "00" Once 4 valid segments are formed and all digits are used, combine them with "." to form a valid IP address. This problem was a great exercise in recursion, constraint validation, and systematic exploration of possibilities. 📈 Progress so far: 🔥 Current Streak: 25 Days 🎯 Goal: 60 Days of Consistent Problem Solving #60DaysOfCode #GeeksforGeeks #ProblemSolving #Python #DSA #CodingChallenge #Consistency #LearningJourney