Let the AI trade for you!

TraderAI is a project which utilizes AI algorithms to create different allocation with portfolios for Crypto Projects, divided by sectors / types, in which users can invest their funds and get a higher return than by just investing into a single Crypto Project!

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Portfolio

Our Recent Works
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Why Choose Us

Token Use Cases

Subscription fees

Users will need to hold a certain amount of tokens to access the features and services provided by the DAPP.

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Transaction fees

Users will need to pay a small transaction fee in tokens for every trade made on the DAPP.

Community incentives

Token holders will be able to receive rewards or bonuses for participating in the community and contributing to the project.

General Roadmap

DEVELOPMENT ROADMAP

Research and Development

Professional Trader Selection

DAPP Development

Testing and Optimization

Launch

Maintenance and Updates

Tokenomics

$TraderAI Tokenomics

Initial token allocation: 55% for the public sale, 5% for the team and advisors, 25% for community incentives & liquidity, 15% for seed round investors.

Isometric Business to Business Marketing, B2B Solution, business marketing concept. Online business, Partnership and Agreement.

Total token supply

1 million tokens (1 000 000)
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Token initial price

$0.3
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Token type

Utility token
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Token standard

BEP-20 (Binance Smart Chain)
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Token Governance

Token Governance

Token holders will have the ability to vote on important decisions related to the project, such as the selection of professional traders, the development of new features, or changes to the algorithm. This will allow the community to have a say in the direction of the project and increase the alignment of interest of the token holders and the project.

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Our Official Partners

Presale Partners

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TraderAI Project

The AI Mechanics
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The AI algorithm in the TraderAI project will work by analyzing historical trading data and identifying patterns and trends that are indicative of successful strategies. This can be done using a combination of supervised learning and reinforcement learning techniques.


AI algorithm performance will also depend on the quality and relevance of the data used to train it, as well as the quality of the algorithm implementation and the parameters chosen. Additionally, the AI algorithm will be continuously monitored and backtested.