Trading Bot Dev for Technology Solutions Company
- API Development Blockchain Web Development
- Confidential
- Aug. - Nov. 2024
- Quality
- 4.5
- Schedule
- 5.0
- Cost
- 5.0
- Willing to Refer
- 5.0
"One standout aspect is their rigorous focus on security-first practices across all stages of development."
- Information technology
- Harrisburg, Pennsylvania
- 1-10 Employees
- Online Review
- Verified
Scytalelabs developed a trading bot for a technology solutions company. The team handled the bot's development, ensuring it could analyze market state, validate token legitimacy, and track trading performance.
Scytalelabs successfully delivered a bot with a fast execution time, meeting the client's expectations and increasing user engagement. The team was responsive and proactive, and their project management was thorough. Moreover, they communicated regularly and made adjustments as needed.
The client submitted this review online.
BACKGROUND
Please describe your company and position.
I am the CEO of AD-T3CH
Describe what your company does in a single sentence.
American Designed Technology is an innovative, for-profit company specializing in the development of cutting-edge technology solutions. We focus on creating advanced tools and software for a range of industries, with expertise in areas such as automated trading bots, cryptocurrency solutions, and mobile, desktop, and console software development. Our company’s mission is to deliver high-performance and scalable technologies that empower our clients to operate more efficiently in the digital age.
OPPORTUNITY / CHALLENGE
What specific goals or objectives did you hire Scytalelabs to accomplish?
- Solona Bot
- Trading Solona Tokens
SOLUTION
How did you find Scytalelabs?
UpWork
Why did you select Scytalelabs over others?
- High ratings
- Pricing fit our budget
- Great culture fit
How many teammates from Scytalelabs were assigned to this project?
2-5 Employees
Describe the scope of work in detail. Please include a summary of key deliverables.
Objectives
Automated Token Management: Automatically monitor, buy, and sell tokens that meet specified trading criteria.
Real-Time Insights: Use up-to-date data to identify profitable trading opportunities and optimize trading decisions.
Security and Risk Management: Integrate data checks to mitigate risks from scams or honey pot tokens, ensuring a safe trading environment.
Required Data from Bitquery
To operate effectively, the Solana Trading Bot requires specific data from Bitquery to analyze market conditions, validate token legitimacy, and monitor trading performance. The data requirements include:
Token and Liquidity Data:
Token Name and Contract Address: For verification and filtering of desired tokens.
Liquidity Pool Information: Includes details on token pairs, pool reserve amounts, and liquidity volume.
Trading Volume: Tracks buy/sell volume over the last 24 hours to determine activity and potential profitability.
Liquidity Changes: To detect any sudden additions or withdrawals in liquidity that could signal pump-and-dump schemes.
Price Data:
Token Price: Current market price and recent price history for calculating price trends and PNL.
Price Changes: Percentage of price increase/decrease within specific time frames (e.g., last 1 hour, 24 hours).
Trailing Price Analysis: To support trailing stop-loss and take-profit mechanisms.
Transaction History:
Recent Trades: Aggregated list of recent trades involving the token, including transaction types (buy/sell), volumes, and participating wallets.
Whale Movements: Identifies large transactions and wallet behavior patterns that could influence price action.
Token Metadata:
Token Holder Distribution: Distribution of token ownership across wallets, helping to assess potential manipulation.
Freeze/Burn Status: Metadata indicating the mint authority status (freeze or burn enabled/disabled).
Honey Pot Check: Verification that a token is not configured to trap buyers by restricting sell functionality.
Plan of Use
The Solana Trading Bot will use the above data from Bitquery in a structured workflow to optimize trading decisions, ensure security, and execute trades based on user-defined criteria. Below is the step-by-step process:
Token Filtering and Validation:
Data Collection: Retrieve token liquidity, price, and metadata from Bitquery.
Validation Check: Use the data to check token legitimacy by validating freeze/burn status and honey pot checks.
User Preferences: Filter tokens based on user criteria such as liquidity minimums, trading volume, and price change thresholds.
Action: Filtered tokens are displayed to the user or automatically queued for the next steps.
Automated Trade Execution:
Price and Liquidity Monitoring: Continuously monitor Bitquery data on token price and liquidity conditions in real-time.
Buy and Sell Triggers: Execute trades when token price, liquidity, and trading volume match user-defined buy/sell triggers.
Risk Mitigation: Implement trailing stop-loss and take-profit mechanisms using Bitquery’s price data to secure gains and limit losses dynamically.
Notification and Reporting:
Trade Alerts: Send real-time notifications through Telegram for each executed buy/sell order, including token details and current PNL.
Price Alerts: Use Bitquery’s price change data to notify users when significant price movements occur, aligning with user-specified price targets.
PNL Tracking: Calculate PNL per trade and over the user’s entire portfolio using token price history and transaction data from Bitquery.
Continuous Risk Assessment:
Liquidity Analysis: Regularly track liquidity changes and token holder distribution to identify potential manipulation or exit scams.
Whale Activity Monitoring: Analyze whale movements for potential price swings. For instance, if a whale initiates a large sell order, the bot can alert the user or pause auto-sniping activities for that token.
Transaction Log and Export:
Transaction Logging: Capture all buy/sell transactions with timestamps, prices, and PNL for record-keeping and audit purposes.
Export Options: Allow users to export their transaction history and performance reports for tax reporting and analysis.
Value Proposition
By leveraging Bitquery’s reliable and comprehensive blockchain data, the Solana Trading Bot provides:
Enhanced Trading Efficiency: Automation of routine trading tasks, enabling users to capitalize on profitable opportunities.
Informed Decisions: Real-time data integration gives users a clear view of market conditions and token performance.
Risk Control: Continuous risk assessments and advanced stop-loss/take-profit mechanisms provide safety measures, particularly in volatile markets.
RESULTS & FEEDBACK
What were the measurable outcomes from the project that demonstrate progress or success?
Performance Metrics
Execution Speed: Measure the average time from identifying a trading signal to executing a buy or sell order. Faster execution time demonstrates that the bot successfully responds to real-time data.
Uptime and Reliability: Track the bot’s uptime (goal: 99% or higher) to ensure consistent availability. Reliability can be assessed by the number of completed transactions without failure.
Trade Success Rate: Analyze the percentage of successful trades (those that meet or exceed the user-defined take-profit levels) out of total trades executed. A higher success rate suggests effective filtering and trigger conditions.
Average PNL per Trade: Report the average profit or loss per trade over a defined period. Positive PNL indicates profitable bot performance.
2. User Engagement Metrics
Active Users and Engagement Rate: Measure the number of unique active users and user actions (e.g., commands, filters set, trades triggered). High engagement indicates that users find the bot useful and easy to interact with.
Real-Time Notification Response Time: Track the response times for notifications (buy/sell alerts, PNL updates). Faster, reliable notifications improve user satisfaction and confidence.
User Retention Rate: Analyze the percentage of users who continue to use the bot over multiple weeks or months. High retention indicates satisfaction and perceived value.
3. Financial Metrics
Total and Monthly PNL (Profit and Loss): Calculate the total profit or loss generated by the bot across all users or within individual user portfolios. This outcome reflects the bot’s direct financial performance.
Average Profit per User: Calculate the average profit for users over a set timeframe to understand the bot’s profitability for individual traders.
Transaction Costs: Report the cumulative transaction fees incurred during trading activities, aiming for efficient execution with minimal cost to maximize net gains.
4. Operational and Security Metrics
Security Incidents or Breaches: Measure the number of security incidents or breaches (e.g., unauthorized access attempts, failed whitelisting). Zero incidents indicate robust security measures.
Error Rate: Track the rate of operational errors (e.g., failed transactions, bugs in executing commands). A low error rate shows stability and reliability in bot performance.
User Satisfaction Ratings: Through user surveys or feedback forms, measure user satisfaction with bot features, speed, and reliability. Positive ratings signal high user approval.
5. Data Accuracy and Market Responsiveness
Accuracy of Market Insights: Measure the bot’s ability to generate accurate market insights and identify profitable tokens (through data validation against Bitquery data).
Response to Market Changes: Track the bot’s responsiveness to major market changes (e.g., liquidity shifts, whale trades) and its effectiveness in adjusting strategies accordingly.
Additional Outcome for Future Enhancements
Feature Utilization Rate: Measure the usage rates of different features, such as trailing stop-loss, re-buy functionality, and watchlist alerts. This data helps identify popular features and prioritize future enhancements.
Describe their project management. Did they deliver items on time? How did they respond to your needs?
Zaid Muni’s project management on the Solana Sniper Bot Project was thorough and responsive. He maintained close oversight of the timeline and deliverables, ensuring that all items were delivered on schedule. Communication was a key strength; he regularly provided progress updates and responded promptly to any questions or requests, making adjustments as needed to address specific requirements or feedback. Zaid’s proactive approach and attention to detail fostered a smooth project workflow and helped meet project goals effectively.
What was your primary form of communication with Scytalelabs?
- Virtual Meeting
- Email or Messaging App
What did you find most impressive or unique about this company?
Scytale Labs’ commitment to project optimization is impressive, especially in how they integrate security, efficiency, and transparency into every project. One standout aspect is their rigorous focus on security-first practices across all stages of development. Their dedication to secure coding, regular vulnerability assessments, and implementing best practices in cryptographic protocols shows a proactive approach to safeguarding user data and project integrity, which is crucial for blockchain-based and high-stakes projects.
Additionally, Scytale Labs excels in agile project execution with an emphasis on continuous improvement. They’re known for iteratively refining their products based on performance data and user feedback, which accelerates optimization and ensures the final deliverable is well-polished and aligned with client needs. This commitment to adaptive project management allows for quick adjustments and high responsiveness, which sets them apart in the tech space.
Are there any areas for improvement or something Scytalelabs could have done differently?
Client-Centric Documentation: Providing more detailed documentation tailored to the client’s knowledge level could help non-technical stakeholders better understand project progress and technical decisions. For example, offering simplified breakdowns or visuals of complex processes could improve engagement and comprehension.
RATINGS
-
Quality
4.5Service & Deliverables
"Very good quality"
-
Schedule
5.0On time / deadlines
"On time"
-
Cost
5.0Value / within estimates
"Best value"
-
Willing to Refer
5.0NPS
