The true potential of #QuantLib: from 100 seconds to Live Risk. Pls PM for Jupyter link.
3 simple steps for using #QuantLib for "Live Risk", including XVA and PFE: 1. Accelerate QuantLib from 100 sec to 0.1 sec* (for a single CVA/DVA calculation). 2. Enable full AAD risk*. 3. Create a "live-risk" service, that takes market moves as input and see how the full risk profile changes in a split second! *Use MatLogica AADC. Jupyter available on request.
3 simple steps for using #QuantLib for "Live Risk", including XVA and PFE: 1. Accelerate QuantLib from 100 sec to 0.1 sec* (for a single CVA/DVA calculation). 2. Enable full AAD risk*. 3. Create a "live-risk" service, that takes market moves as input and see how the full risk profile changes in a split second! *Use MatLogica AADC. Jupyter available on request.
Excited to share that I’ve just completed the "Arranging and Visualizing Data in R" course on Coursera! 🎉 This course has strengthened my skills in data wrangling, visualization, and working with R libraries like ggplot2 and dplyr. Looking forward to applying these skills in future projects! #DataScience #RProgramming #DataVisualization #Learning https://lnkd.in/dg3TgR45
Application developer | Flutter | Java | UI-UX | Data structures & Algorithms | BCA'25
Day 16 & 17 of #60dayscodingchallenge I solved a unique problem of Binary Tree, Finding Top View of a tree. The approach is, firstly find horizontal distance of each node and traverse the tree using level order traversal. Make a HashMap which store key value pair of horizontal distance as key(uniquely) and node data as value. The values stored in HashMap are the top view of tree. #DSA #60DaysCodeChallenge #BinaryTree #HashMap
I highly recommend you read the below article, written by my esteemed colleague, Roger Ahanonu. In this piece, Roger delves into how high-quality historical data from BMLL can provide unparalleled insights into Dark and Addressable [and Non-Addressable] liquidity trends. For instance, when reviewing the intraday liquidity volume profile over a 6-month period in Europe, excluding uncrossing periods reveals the significant size of non-addressable special price component, as a proportion of volumes. The article provides multiple examples of how leveraging the BMLL Data Lab enables you to effectively pinpoint where dark liquidity and addressable volume are concentrated, enabling precise trading decisions. I'm currently sharpening my Python skills, but if you're interested in seeing these insights in action or have any questions, I can put in a good word with Roger and our team to arrange a time to discuss further.
For trading practitioners, it is essential to easily identify addressable, versus non-addressable liquidity, and isolate relevant liquidity types. You need to access both normalised Market States, and Trade Type fields such as Lit, Dark, OTC. The BMLL Data Lab offers rapid access to Level 2 and Trades Market Data within a Python Jupyter notebook environment. You can: ✅conduct efficient analysis ✅quickly generate volume profiles for individual instruments, indices, or across the entire market ✅plan strategies targeting specific types of liquidity ✅understand the nuances of intraday volume trends to achieve targeted benchmark performance You can effectively pinpoint where dark liquidity and addressable volume are concentrated, enabling precise trading decisions. https://hubs.li/Q02L7wkG0 Contact us for your BMLL Data Lab demo today. #HistoricalDataDoneProperly #BetterInputForTradingStrategies #IncreaseExecutionAlpha
Tradingview Pinescript – Real-Time Option Greeks with Implied Volatility Using Claude Sonnet 3.5 (newer model) https://lnkd.in/gyg99kWg
Day 10: Vectors and HashMaps Unlock the power of Vectors and HashMaps in Rust! These dynamic data structures allow you to handle collections efficiently, with Vectors acting as growable arrays and HashMaps enabling quick key-value lookups. Perfect for developers aiming to manage flexible data. 💻 Code examples + exercises included! https://lnkd.in/gpvdvW8b #RustLang #SoftwareDevelopment #LearnToCode #Rust
Empowering businesses to harness the full potential of data | Best-Selling Author | Founder of Mastering Snowflake Program
In this video I show you how to manage your reference data efficiently using #dbt seed files, ensuring consistency and reliability in your data transformations. What You'll Learn: ►Introduction to dbt Seed Files ►Creating Seed Files ►Loading Seed Files ►Using Seed Files in Transformations ►Maintaining Seed Files ►Benefits of Using Seed Files Explore the advantages of using seed files, such as improved data consistency and simplified management.
QuestDB finally introduces multi-threaded execution for SAMPLE BY queries ⚡ The following query spans 1 year of intraday crypto market data and includes multiple aggregations and a filter. QuestDB processes 105 Million rows, and the query produces an output of 35K rows. Total query execution time? 477 milliseconds 🤯 Try it yourself via the #QuestDB Live Demo (link provided in the comments below)! #SQL #Downsampling #vectorized #JIT #SIMD #opensource #marketdata #database
#Day 22 of my 30-day problem-solving challenge: Today, I solved the problem of detecting a cycle in a linked list. A cycle exists if, by following the `next` pointers, we can revisit a node that we've already encountered. This problem is typically solved using Floyd’s Tortoise and Hare algorithm, where two pointers move at different speeds. If there is a cycle, they will eventually meet. If no cycle exists, the faster pointer will reach the end of the list without looping back. Detecting cycles in linked lists is important in avoiding infinite loops in data structures and algorithms.
