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Machine Learning in Trading

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Filter:Machine Learning in Trading

Category – Machine Learning in Trading

Machine learning in trading refers to the use of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without explicit programming. In trading, machine learning is used to analyze market data, identify patterns, and develop predictive models for asset pricing, risk management, and trade execution. Traders use machine learning for algorithmic trading, portfolio optimization, and sentiment analysis, aiming to automate strategies and improve accuracy in decision-making.

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MERKLE TREE

Merkle tree is a data structure used in blockchain technology to efficiently and securely verify data.

INITIAL STAKE POOL OFFERING (ISPO)

Initial Stake Pool Offering (ISPO) is a fundraising mechanism where investors delegate their stakes to a stake pool to support a project.

HALVING (BITCOIN)

Halving (Bitcoin) is the event where the reward for mining new blocks is halved, which occurs approximately every four years.

HARD FORK

Hard fork is a radical change to the protocol of a blockchain network that makes previously invalid blocks/transactions valid.

GENESIS BLOCK

Genesis block is the first block in a blockchain.

ESCROW (CRYPTO)

Escrow (crypto) is a third-party service that holds and regulates payment of funds in a transaction.

ENCRYPTION

Encryption is the process of converting information or data into a code to prevent unauthorized access.

ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM (ECDSA)

Elliptic Curve Digital Signature Algorithm (ECDSA) is a cryptographic algorithm used by Bitcoin to ensure that funds can only be spent by their rightful owners.

DAPP (DECENTRALIZED APPLICATION)

DApp (decentralized application) is an application that runs on a decentralized network, avoiding a single point of failure.

CRYPTOGRAPHIC HASH FUNCTION

Cryptographic hash function is an algorithm that takes an input and produces a fixed-size string of bytes.

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