Fix Solana Arbitrage Bot
Budget: €12 – €18 EUR
I need help to get my Solana arbitrage bot live. The bot is currently built in Python and runs on PowerShell.
The bot already includes:
DEX-to-DEX arbitrage (Jupiter + fallback to Raydium)
Triangular arbitrage logic (mock works, live version inactive)
Vault system with balance tracking
Auto-compounding logic (win → reinvested)
AI prediction layer (internal & external)
Telegram alerts
Async architecture with parallel tasks
Goal:
Make the bot profitable, adaptive and fully autonomous, with:
Live triangular & DEX arbitrage
Realistic execution cost tracking
AI-driven trading decisions
Continuous reinvestment of profits (auto-compounding) to enable exponential growth
24/7 uptime
Current Issues:
Logic & Execution:
object str can't be used in 'await' expression – breaking flow after mock Raydium swap
Triangular arbitrage logic is present but not triggered in main loop
Vault profit tracking broken — high “profits” are not reflected in the balance
Slippage is modeled too aggressively (linear instead of compound)
Unrealistic Profits:
Mock environment shows €700+ "profits" per trade on a small vault — this leads to negative balance due to untracked costs
AI Issues:
Dummy AI (returns 0.4–0.7 randomly)
Thresholds are too low (0.5–0.6) → bad trades are allowed
No learning/feedback mechanism yet
The AI module should self-learn over time from market behavior and trading history to adapt to volatility and opportunity quality
Auto-Compounding Incomplete:
Profits are added to the vault, but not effectively used in future trades
Need true auto-compounding logic: each profitable trade should increase future trade size, triggering exponential capital growth
PnL Tracking Missing:
No real-time profit/loss overview per trade
No way to track effectiveness of strategy over time
Operational:
No Docker or persistent execution setup (e.g. screen, daemon, watchdog)
Code runs only via PowerShell – no deployment script or Linux compatibility
AI Functionality Goal:
Combine internal AI (trend + volatility scoring) and external AI API
Use weighted logic to filter trades
System must learn over time by adjusting thresholds and recognizing patterns
Objective: Self-optimizing bot that becomes smarter with each execution
Auto-Compound Objective:
After each successful trade, profit is reinvested
Trade size should increase proportionally to capital growth
Enables exponential portfolio scaling over time without manual intervention
The bot already includes:
DEX-to-DEX arbitrage (Jupiter + fallback to Raydium)
Triangular arbitrage logic (mock works, live version inactive)
Vault system with balance tracking
Auto-compounding logic (win → reinvested)
AI prediction layer (internal & external)
Telegram alerts
Async architecture with parallel tasks
Goal:
Make the bot profitable, adaptive and fully autonomous, with:
Live triangular & DEX arbitrage
Realistic execution cost tracking
AI-driven trading decisions
Continuous reinvestment of profits (auto-compounding) to enable exponential growth
24/7 uptime
Current Issues:
Logic & Execution:
object str can't be used in 'await' expression – breaking flow after mock Raydium swap
Triangular arbitrage logic is present but not triggered in main loop
Vault profit tracking broken — high “profits” are not reflected in the balance
Slippage is modeled too aggressively (linear instead of compound)
Unrealistic Profits:
Mock environment shows €700+ "profits" per trade on a small vault — this leads to negative balance due to untracked costs
AI Issues:
Dummy AI (returns 0.4–0.7 randomly)
Thresholds are too low (0.5–0.6) → bad trades are allowed
No learning/feedback mechanism yet
The AI module should self-learn over time from market behavior and trading history to adapt to volatility and opportunity quality
Auto-Compounding Incomplete:
Profits are added to the vault, but not effectively used in future trades
Need true auto-compounding logic: each profitable trade should increase future trade size, triggering exponential capital growth
PnL Tracking Missing:
No real-time profit/loss overview per trade
No way to track effectiveness of strategy over time
Operational:
No Docker or persistent execution setup (e.g. screen, daemon, watchdog)
Code runs only via PowerShell – no deployment script or Linux compatibility
AI Functionality Goal:
Combine internal AI (trend + volatility scoring) and external AI API
Use weighted logic to filter trades
System must learn over time by adjusting thresholds and recognizing patterns
Objective: Self-optimizing bot that becomes smarter with each execution
Auto-Compound Objective:
After each successful trade, profit is reinvested
Trade size should increase proportionally to capital growth
Enables exponential portfolio scaling over time without manual intervention