# Trading Published articles for Trading. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Node.js and TypeScript trading engine with shared backtest and live execution DevFeed: [A Node.js and TypeScript trading engine with shared backtest and live execution]() Original publisher: [Read original article]() Author: tripolskypetr Published: 2026-09-17T16:48:40Z Content type: article Language: ru Sources: [Tagir Valeev]() Topics: [Node.js](), [TypeScript](), [API](), [execution](), [Persistence](), [Risk]() Tags: [api](), [execution](), [js](), [metatrader](), [node-js](), [nodejs](), [persistence](), [pinescript](), [quant](), [risk](), [tag-6c418f2015ec](), [tag-6f3b8e7b0ba0](), [tag-74101175889a](), [tag-bd01f5da93d2](), [tag-be7ce795b781](), [trading](), [tradingview](), [typescript]() ### AI overview The article presents a Node.js and TypeScript framework for algorithmic trading. It uses the same execution flow for backtesting and live trading, while modeling trade lifecycles and providing API-level protections against look-ahead bias and crash-safe persistence for live and paper modes. ### Source excerpt πŸ’» Π’ экосистСмС Node.js ΠΏΡΡ‚ΡŒ Π»Π΅Ρ‚ Π½ΠΈΡ‡Π΅Π³ΠΎ Π½ΠΎΠ²ΠΎΠ³ΠΎ, Π½ΡƒΠΆΠ½ΠΎ ΠΈΡΠΏΡ€Π°Π²ΠΈΡ‚ΡŒ Появился Ρ„Ρ€Π΅ΠΉΠΌΠ²ΠΎΡ€ΠΊ, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ позволяСт ΠΏΠΈΡΠ°Ρ‚ΡŒ quant trading Π½Π° TypeScript Π±Π΅Π· пСрСписывания ΠΊΠΎΠ΄Π° ΠΏΡ€ΠΈ запускС ΠΈΠ· backtest Π² paper ΠΈΠ»ΠΈ live. Π’ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ‚Π΅ встроСнный ΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠΉ интСрфСйс, Π·Π°Ρ‰ΠΈΡ‚Π° ΠΎΡ‚ look-ahead bias Π½Π° ΡƒΡ€ΠΎΠ²Π½Π΅ API, crash-safe persistence для live ΠΈ paper, risk-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΠΌΠ΅Π½Ρ‚ Π½Π° ΡƒΡ€ΠΎΠ²Π½Π΅ портфСля Π§ΠΈΡ‚Π°Ρ‚ΡŒ Π΄Π°Π»Π΅Π΅ ## How Jump Trading uses ClickHouse with Iceberg for analytics DevFeed: [How Jump Trading uses ClickHouse with Iceberg for analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Apache Iceberg](), [log management](), [data](), [Latency](), [Open Source](), [real-time](), [Database]() Tags: [analytics](), [apache](), [apache-iceberg](), [business](), [clickhouse](), [data](), [database](), [industry](), [latency](), [logging](), [logs](), [low-latency](), [observability](), [open](), [open-source](), [performance](), [real-time](), [scale](), [technology](), [trading]() ### AI overview Jump Trading uses a self-managed ClickHouse platform to capture and analyze petabyte-scale financial trading logs. The platform ingests hundreds of terabytes daily with a sub-20-second p99 and supports real-time analytics across hundreds of billions of events. To support large-scale batch reporting and research without affecting the real-time cluster, Jump added a parallel Apache Iceberg pipeline. ### Source excerpt Jump Trading captures petabyte-scale financial trading logs on a self-managed ClickHouse platform, where zero data loss and low latency are critical requirements. ## How Bullet uses ClickHouse Cloud to give DeFi's fastest exchange real-time analytics DevFeed: [How Bullet uses ClickHouse Cloud to give DeFi's fastest exchange real-time analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Latency](), [real-time](), [databricks](), [Blockchain](), [data](), [Monitoring](), [Ethereum]() Tags: [analytics](), [architecture](), [blockchain](), [clickhouse](), [cloud](), [cost](), [crypto](), [databricks](), [finance](), [latency](), [monitor](), [monitoring](), [platform](), [real-time](), [speed](), [trading]() ### AI overview Bullet uses ClickHouse Cloud to index, analyze, and monitor its real-time DeFi perpetuals exchange. The migration from Databricks reduced query latency from 10-15 seconds to milliseconds, improved data freshness to under five seconds, eliminated a separate serving layer, and supported 1,000 times more data at comparable cost. ### Source excerpt Switching from Databricks to ClickHouse Cloud cut Bullet's query latency 10,000x and eliminated an entire serving layer, giving DeFi's fastest exchange real-time analytics at comparable cost. ## LABScon25 Replay | Breach Alpha: Trading on Cyber Fallout DevFeed: [LABScon25 Replay | Breach Alpha: Trading on Cyber Fallout]() Original publisher: [Read original article]() Author: LABScon Published: 2026-05-14T13:00:44Z Content type: article Language: en Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.]() Topics: [Cybersecurity](), [LABScon](), [incident](), [Artificial Intelligence](), [Time Series](), [dataset](), [ransomware]() Tags: [ai](), [breach](), [cybersecurity](), [data](), [labscon](), [labscon25](), [model](), [ransomware](), [time-series](), [trading]() ### AI overview Mick Baccio and Scott Roberts examine whether public breach signals can anticipate stock-market reactions before formal disclosure. Using AI-assisted data collection, a public-disclosure dataset, an intuition-led model, and Hidden Markov Model time-series analysis, they test a "15/30" cyber-event trading hypothesis and find highly mixed results. ### Source excerpt Mick Baccio and Scott Roberts examine whether public breach signals and market timing models can turn cyber incidents into actionable trading opportunities. ## Putting the Tech in Fintech: How We Use FIX to Execute Trades at Scale DevFeed: [Putting the Tech in Fintech: How We Use FIX to Execute Trades at Scale]() Original publisher: [Read original article]() Author: Grace Sun Published: 2026-04-22T00:27:03Z Content type: article Language: en Sources: [Wealthfront]() Topics: [distributed-systems](), [networking](), [Transactions](), [Back end](), [IP routing](), [TCP/IP]() Tags: [architecture](), [backend](), [distributed](), [financial](), [infrastructure](), [networking](), [route](), [scale](), [systems](), [tcp-ip](), [tech](), [technology](), [trading](), [transactions](), [wealthfront-engineering]() ### AI overview Wealthfront explains the distributed-systems infrastructure it uses to route and execute large volumes of stock trades. The article describes broker networking, IP routing, the FIX protocol, and the application layers supporting automated, low-latency trade execution. ### Source excerpt At Wealthfront, the "tech" in financial technology isn't just a buzzword--it's the foundation of everything we build. Beneath the intuitive frontend our clients interact with lies a complex ecosystem of distributed systems. One of the most critical pieces of that backend architecture is the engine that enables us to route massive volumes of trades efficiently. ... Read more ## Why FinTech Is Moving to Distributed SQL Databases DevFeed: [Why FinTech Is Moving to Distributed SQL Databases]() Original publisher: [Read original article]() Author: David Weiss Published: 2025-12-12T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Databases](), [SQL](), [systems](), [Transactions](), [consistency](), [Resilience](), [Availability]() Tags: [acid](), [availability](), [compliance](), [consistency](), [databases](), [distributed](), [fintech](), [global](), [legacy](), [nosql](), [payments](), [resilience](), [sql](), [trading](), [transactions]() ### AI overview This article explains why FinTech organizations are considering distributed SQL databases as they scale financial workloads across regions and clouds. It emphasizes correctness, consistent ACID transactions, resilience, availability, regulatory compliance, and the limitations of legacy relational and patchwork NoSQL systems. ### Source excerpt FinTech teams are operating under more pressure than ever. As digital financial services become the default for consumers and enterprises alike, the margin for error has all but disappeared. Users expect every interaction to be instantaneous, accurate, and always available, with very little patience for anything less. ## Building StockHouse: Real-time market analytics with ClickHouse DevFeed: [Building StockHouse: Real-time market analytics with ClickHouse]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2025-11-13T16:21:22Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Streaming](), [Time Series](), [Go Language](), [WebSocket](), [Node.js](), [Vue.js](), [dashboards](), [Back end](), [Front end]() Tags: [architecture](), [backend](), [clickhouse](), [engineering](), [financial](), [go](), [js](), [low-latency](), [node](), [observability](), [real-time](), [series](), [trading](), [vue](), [vue-js](), [web]() ### AI overview This article presents StockHouse, a complete demo for building a real-time market analytics application. It uses Massive WebSocket APIs for live stock and crypto data, ClickHouse for efficient storage and low-latency analysis, and Perspective for interactive visualization. ### Source excerpt Learn more how we built StockHouse, a real-time financial analytics application with Massive, ClickHouse and Perspective that scales to thousands of events per second. ## How to automate earning and selling Steam Trading Cards DevFeed: [How to automate earning and selling Steam Trading Cards]() Original publisher: [Read original article]() Author: Jake Lee Published: 2025-10-10T23:00:00Z Content type: tutorial Language: en Sources: [Jake Lee's Programming Blog]() Topics: [Automation](), [Software]() Tags: [automated](), [automation](), [guide](), [steam](), [trading]() ### AI overview A tutorial on using automation software, specifically Idle Master Extended, to earn Steam Trading Cards from eligible games and sell them on the Steam Market. It also discusses checking card eligibility and assessing software safety, though the supplied text ends mid-article. ### Source excerpt Steam Trading Cards have been out for a long time, but you probably didn't realise you likely have tens of games with earning potential just sitting in your library! Here's how to earn and sell the cards quickly. ## A Higgs-bugson in the Linux Kernel DevFeed: [A Higgs-bugson in the Linux Kernel]() Original publisher: [Read original article]() Author: Nikhil Jha Published: 2025-07-02T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [bug](), [Kernel](), [Linux](), [debug](), [Filesystems](), [POSIX](), [Security]() Tags: [bug](), [data](), [debug](), [filesystem](), [kernel](), [linux](), [linux-kernel](), [posix](), [security](), [trading]() ### AI overview A Jane Street engineering post investigates a difficult-to-reproduce bug affecting large file copies in Gord, a system that stores and distributes trading activity data. It explains the role of NFS, Kerberos authentication, and Linux kernel credential handling during the debugging process. ### Source excerpt We recently ran across a strange higgs-bugson that manifested itself in a critical system that stores and distributes the firm's trading activity data, called Gord. (A higgs-bugson is a bug that is reported in practice but difficult to reproduce, named for the Higgs boson, a particle which was theorized in the 1960s but only found in 2013.) In this post I'll walk you through the process I took to debug it. I tried to write down relevant details as they came up, so see if you can guess what the bug is while reading along. ## Best Loser Wins: A Review of Tom Hougaard's Trading Mindset and Discipline DevFeed: [Best Loser Wins: A Review of Tom Hougaard's Trading Mindset and Discipline]() Original publisher: [Read original article]() Author: Tom Published: 2025-01-23T00:00:00Z Content type: article Language: en Sources: [Tomasz GΔ…gor]() Topics: [Resilience]() Tags: [advice](), [analysis](), [finance](), [financial](), [investing](), [journey](), [psychology](), [resilience](), [trading]() ### AI overview A review of Tom Hougaard's Best Loser Wins, which focuses on trading psychology, discipline, cutting losses early, and accepting losses rather than offering technical analysis or a formula for success. The reviewer finds its lessons useful but notes that the writing can be repetitive and that some strategies are less applicable to long-term investors. ### Source excerpt Best Loser Wins Why Normal Thinking Never Wins the Trading Game - Written by a High-Stake Day Trader Author: Tom Hougaard Tom Hougaard's "Best Loser Wins" offers a refreshingly honest look at the realities of trading. It's not a traditional "how-to" guide packed with technical analysis or a magic formula for success. Instead, it's an introspective journey of a trader, highlighting the mindset and discipline required to navigate the often unforgiving world of financial markets. ## Using Trino with Apache Airflow for (almost) all your data problems DevFeed: [Using Trino with Apache Airflow for (almost) all your data problems]() Original publisher: [Read original article]() Author: Philippe Gagnon, Brian Olsen Published: 2022-12-21T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [airflow](), [data](), [Data Infrastructure](), [Scalability](), [Availability](), [Databases](), [Software as a service]() Tags: [airflow](), [analytics](), [apache](), [architecture](), [availability](), [batch](), [data](), [data-infrastructure](), [databases](), [integration](), [saas](), [scalability](), [summit](), [trading](), [warehouse](), [workflow]() ### AI overview The article recaps a Trino Summit 2022 talk about using Apache Airflow to orchestrate Trino batch queries. It explains Trino's fault-tolerant execution mode and discusses moving queries closer to federated data sources to improve data availability, performance, scalability, and collaboration. ### Source excerpt As we close in on the final talks from Trino Summit 2022, this next talk dives into how to set up Trino for batch processing. Trino has historically been well-known for facilitating fast adhoc analytics queries as opposed to long-running, resource intensive batch/ETL queries. This is due to the fact that Trino kills queries that run out of resources in order to prioritize faster query execution. Earlier this year, Trino added features to better support batch queries with a new fault-tolerant execution mode. This mode backs up intermediate data during execution time, allowing Trino to restart individual query tasks on failure rather than a query stage or the query itself. Batch queries don't typically involve human intervention and run asynchronously. These tasks may depend on each other and have a complex workflow. This talk describes how to orchestrate this complexity using Airflow's new Trino integration to run Trino batch queries to solve (almost) all your data problems. ## No Risk No Reward DevFeed: [No Risk No Reward]() Original publisher: [Read original article]() Published: 2015-08-18T00:00:00Z Content type: opinion Language: en Sources: [Signal Blog]() Topics: [systems]() Tags: [communication](), [finance](), [learning](), [leaving](), [trading]() ### AI overview A finance professional reflects on leaving options market making and algorithmic trading to join the Open Whisper Systems team and support secure, private communication. The transition is challenging but rewarding, requiring continuous learning across technical subjects and user advocacy. ### Source excerpt After immersing myself for years and years in the world of finance (specifically, options market making and algorithmic trading), there was no escaping this mantra of "no risk, no reward." Most of the time, my (trading) style was fairly risk-averse. I knew how to find the signals of opportunities among the daily noise to reap small rewards. Fortunately, I also learned how to think about the outliers and to take the occasional big but calculated risks. Read more... ## Bandits and Stocks DevFeed: [Bandits and Stocks]() Original publisher: [Read original article]() Published: 2013-12-09T09:00:52Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Programming](), [Machine Learning & Artificial Intelligence]() Tags: [algorithm](), [algorithms](), [bandit-learning](), [bandits](), [exp3](), [experimental](), [finance](), [machine-learning](), [programming](), [python](), [stocks](), [trading](), [ucb1]() ### AI overview This article experiments with the UCB1 and Exp3 bandit-learning algorithms on stock-trading data to compare their performance under stochastic and adversarial assumptions. It emphasizes that the model is simplistic and does not solve stock trading. ### Source excerpt So far in this series we've seen two nontrivial algorithms for bandit learning in two different settings. The first was the UCB1 algorithm, which operated under the assumption that the rewards for the trials were independent and stochastic. That is, each slot machine was essentially a biased coin flip, and the algorithm was trying to find the machine with the best odds. The second was the Exp3 algorithm, which held the belief that the payoffs were arbitrary.