# stocks

Published articles for stocks.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Influence, Stocks, and Onboarding

DevFeed: [Influence, Stocks, and Onboarding](<https://devfeed.tech/articles/influence-stocks-and-onboarding-37386.md>)

Original publisher: [Read original article](<https://email.jointaro.com/p/influence-stocks-and-onboarding>)

Author: Rahul Pandey

Published: 2022-10-26T13:46:33Z

Content type: opinion

Language: en

Sources: [Alex Chiou](<https://devfeed.tech/sources/alex-chiou.md>)

Topics: [Software](<https://devfeed.tech/topics/software.md>), [App](<https://devfeed.tech/topics/app.md>), [Android](<https://devfeed.tech/topics/android.md>), [Google](<https://devfeed.tech/topics/google.md>), [Refactoring](<https://devfeed.tech/topics/refactoring.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [app](<https://devfeed.tech/tags/app.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [influence](<https://devfeed.tech/tags/influence.md>), [onboarding](<https://devfeed.tech/tags/onboarding.md>), [software](<https://devfeed.tech/tags/software.md>), [stocks](<https://devfeed.tech/tags/stocks.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

This developer newsletter discusses building organizational influence as a software engineer, managing emotions around employer stock prices, and onboarding to a new team. It also recommends a practical newsletter for developing technology leadership skills.

### Source excerpt

Hey everyone 👋🏽 Today we'll cover:

## Bandits and Stocks

DevFeed: [Bandits and Stocks](<https://devfeed.tech/articles/bandits-and-stocks-40335.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/12/09/bandits-and-stocks/>)

Published: 2013-12-09T09:00:52Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Programming](<https://devfeed.tech/topics/programming.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [bandit-learning](<https://devfeed.tech/tags/bandit-learning.md>), [bandits](<https://devfeed.tech/tags/bandits.md>), [exp3](<https://devfeed.tech/tags/exp3.md>), [experimental](<https://devfeed.tech/tags/experimental.md>), [finance](<https://devfeed.tech/tags/finance.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [stocks](<https://devfeed.tech/tags/stocks.md>), [trading](<https://devfeed.tech/tags/trading.md>), [ucb1](<https://devfeed.tech/tags/ucb1.md>)

### 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.