Neurolana – AI powered trading bot for Solana capable of copy trading, token sniping and backrunning using stochastic models and risk scores for secure and intelligent trade opportunities

Abstract

This thread introduces Neurolana, an advanced trading bot lever-

aging artificial intelligence to predict trading patterns and devise innova-

tive strategies on the Solana blockchain. By combining the high-speed,

low-cost advantages of Solana with cutting-edge AI algorithms, Neurolana

aims to revolutionize automated trading in the cryptocurrency market.

This paper outlines the project’s core features, including AI-driven pat-

tern recognition, strategy development, copy trading, and token sniping,

as well as addressing the technical and security challenges inherent in such

a system.

1 – Introduction

The cryptocurrency market has experienced exponential growth and volatility

in recent years, creating both opportunities and challenges for traders. Tradi-

tional trading bots, while useful, often struggle to adapt to the rapid changes

and complex patterns in this dynamic environment. Neurolana aims to ad-

dress these limitations by harnessing the power of artificial intelligence and the

high-performance Solana blockchain to create a next-generation trading bot.

Neurolana targets a pool of 200K active traders each averaging a trading vol-

ume of 1000 dollars a month, accessing a 200 million dollar market exploitable

for trading profit.

2 – Project Overview

Neurolana is designed to be a comprehensive trading solution that combines

advanced AI algorithms with the speed and efficiency of the Solana blockchain.

The project’s primary objectives are:

ˆ To develop AI models capable of accurately predicting trading patterns in

real-time

ˆ To create and optimize trading strategies based on AI-generated insights

1

ˆ To implement secure and efficient copy trading functionality targeting high

performing traders

ˆ To develop advanced token sniping capabilities for early investment op-

portunities

ˆ To ensure high security and reliability standards throughout the system

3 – Key Features

3.1 AI-Driven Pattern Recognition

At the core of Neurolana is a sophisticated machine learning model (based on

rust-bert for NLP and BLAS for statistical models) trained on vast amounts

of historical and real-time market data provided by Helius gRPC service. This

model utilizes deep learning techniques, including recurrent neural networks

(RNNs) and transformer architectures, to identify complex patterns and trends

in trading data. By continuously learning from new data, the AI can adapt to

changing market conditions and improve its predictive accuracy over time.

3.2 Dynamic Strategy Development

Building upon the insights generated by the pattern recognition system, Neu-

rolana employs reinforcement learning algorithms to develop and refine trading

strategies. These strategies are continuously evaluated and optimized based

on their performance in simulated and real-world trading scenarios. The sys-

tem can generate a diverse range of strategies, from conservative to aggressive,

catering to different risk profiles and market conditions.

3.3 Copy Trading

Neurolana incorporates a robust copy trading feature, allowing users to auto-

matically replicate the trades of successful traders or AI-generated strategies.

This system utilizes smart contracts on the Solana blockchain to ensure trans-

parent and efficient execution of copied trades. Advanced risk management tools

are integrated to protect users from excessive losses and to maintain a balanced

portfolio.

3.4 Token Sniping

To capitalize on early investment opportunities, Neurolana includes a token

sniping module. This feature uses natural language processing (NLP) to ana-

lyze social media, news sources, and blockchain data to identify promising new

tokens or projects. The AI assesses factors such as team credibility, technol-

ogy innovation, and market sentiment to make informed decisions on potential

investments. The AI also analyzes for criteria such as whether the freeze au-

thority has been relinquished (preventing the token creator from freezing others

accounts) to estabilish a risk score for tokens.

4 – Technical Challenges and Solutions

4.1 – Real-Time Data Processing

Challenge: Processing vast amounts of real-time market data without introduc-

ing latency.

Solution: Neurolana leverages Solana’s high-throughput capabilities and im-

plements a distributed stream processing architecture using technologies like

Apache Kafka and Apache Flink. This allows for efficient real-time data inges-

tion and processing, ensuring that the AI models receive up-to-date information

for accurate predictions.

4.2 – Model Accuracy and Adaptability

Challenge: Maintaining high prediction accuracy in a volatile and ever-changing

market.

Solution: Neurolana employs ensemble learning techniques, combining mul-

tiple AI models to improve overall accuracy and robustness. Additionally, the

system uses online learning algorithms that allow models to be updated in real-

time as new data becomes available, ensuring adaptability to market shifts.

4.3 – Scalability

Challenge: Handling a growing user base and increasing trading volumes with-

out compromising performance.

Solution: The project utilizes a microservices architecture deployed on a

cloud-native infrastructure, allowing for easy scaling of individual components.

Auto-scaling policies are implemented to dynamically adjust resources based on

demand, ensuring consistent performance during peak trading periods.

5 – Security Challenges and Solutions

5.1

Smart Contract Vulnerabilities

Challenge: Ensuring the security of smart contracts that handle user funds and

execute trades.

Solution: Neurolana employs rigorous smart contract auditing processes,

including static analysis, dynamic analysis, and formal verification. Multiple

independent security firms are engaged to conduct thorough audits before de-

ployment. Additionally, the project implements upgradeable smart contract

patterns to allow for security patches without disrupting user funds.

5.2 Private Key Management

Challenge: Securely managing user private keys for trade execution.

Solution: Neurolana uses a combination of hardware security modules (HSMs)

and multi-party computation (MPC) techniques to manage private keys. This

approach ensures that no single point of failure can compromise user funds.

Users also have the option to integrate with hardware wallets for an additional

layer of security. Ledger will be the first one to be supported.

5.3 – Front-Running Protection

Challenge: Preventing malicious actors from front-running trades based on AI

predictions.

Solution: The project implements a commit-reveal scheme for trade exe-

cution, where trade intentions are first committed in an encrypted form and

later revealed for execution. This, combined with Solana’s fast block times,

significantly reduces the window of opportunity for front-running attacks.

Jito’s bundling feature will be featured in order to prevent MEV botting.

As Jito have already disabled the simulateBundle RPC call and the ”mempool”

part of their Geyser service, Neurolana is provided another layer of security from

MEV attempts.

6 – Regulatory Compliance

Neurolana is committed to operating within regulatory frameworks across dif-

ferent jurisdictions. Legal presence in Estonia has already been estabilished and

there are plans to expand to US. A dedicated compliance team monitors and

adapts to regulatory changes, ensuring the platform’s long-term sustainability.

7 – Profit Distribution

Neurolana works akin to an Automated Liquidity Manager, leveraging users

funds to make trades and distributing pay-outs based on the user’s share in the

trade. For example, if an user has contributed to 20 percent of the capital for a

trade with the profit of 2 SOL, the user will receive a pay-out of 0.4 SOL.

8 – Conclusion

Neurolana represents a significant leap forward in the realm of cryptocurrency

trading bots. By combining the power of artificial intelligence with the efficiency

of the Solana blockchain, the project aims to provide traders with a sophisti-

cated, secure, and highly adaptable trading solution. As the cryptocurrency

market continues to evolve, Neurolana is positioned to lead the way in auto-

mated trading innovation, offering users the tools they need to navigate this

complex and dynamic landscape successfully.