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Commodity Trading

Days 1-15: Domain Understanding & System Design​

  • Domain Overview: Study the commodity trading lifecycle - from listing commodities, buyer inquiries, negotiation, to closing deals. Identify unique requirements: multi-party communication, compliance (e.g., trade regulations), and high-speed decision making.

  • Requirements Breakdown: List key modules: Rolodex for traders, Commodity Listings, Automated Matchmaking, Quotation tool, Order execution, WhatsApp/Telegram integration for operations, multi-geo support, and operational CRM for tracking all activities.

  • Architecture Blueprint: Design a scalable architecture that could handle multiple geographies. Consider a microservice for matching engine, a service for messaging integration, and a core CRM service. Plan for integration points (messaging services calling into backend, etc.).

  • Deliverables: Detailed architecture diagram and written design explaining each module and how they interact. A list of technical assumptions (e.g., expected number of users, geographies supported) guiding design decisions. Early selection of technologies to be used, noted in the design doc.

Days 16-30: Contact Management & User Onboarding​

  • Rolodex Module: Implement a contact management system for all parties (buyers, sellers, brokers, ops team). Extend it to handle company entities since many traders act on behalf of organizations. Include fields for verification status (to be used later) and region (country, time zone).

  • User Accounts & Roles: Set up basic user authentication and differentiate roles (e.g., an Ops user with full system access, vs a Buyer/Seller who might have a portal view or just interacts via messaging). Prepare the groundwork for permissioning in later modules.

  • UI/UX for Contacts: Create pages or views for listing contacts, searching by name/company, and viewing contact details (with transaction history placeholder for future). Ensure quick navigation since ops may need to pull up a trader’s info during negotiations rapidly.

  • Deliverables: Functional Rolodex/contacts database with a couple of example buyer/seller profiles. A simple login system with at least an “Ops Admin” role. Screenshot of a contact detail page showing the structure (even if minimal data now). Documentation of how new users (traders) will be onboarded into the system (manually by ops for now, or self-registration workflow planned).

Days 31-45: Commodity Listings & Catalog​

  • Product Listing Schema: Design database tables for commodities and listings. Each listing should capture commodity type (using standardized categories), quantity available, price (or price benchmark), location/origin, and any quality specifications.

  • Listing Management: Implement backend APIs or services for sellers to create and manage listings. Include validity period or expiration for listings if applicable. On the buy side, create a mechanism for buyers to post “requests” or requirements (wanted commodities).

  • Search & Browse UI: Develop a frontend interface for the ops team (or buyers, if applicable) to search available commodity listings by filters (commodity type, location, price range). Provide quick view of listing details and a way to bookmark or mark interesting listings.

  • Deliverables: A populated commodity catalog with sample listings (e.g., 100 tons of Copper Cathodes at $X/ton, available from Location Y). Working search functionality demonstrated with a screenshot (e.g., filter by “Copper” returns relevant listings). Documentation listing commodity categories supported and any standards followed (for example, using ISO commodity codes if any).

Days 46-60: Trader Verification & Compliance​

  • KYC & Verification: Before enabling deals, implement KYC for traders. Integrate an identity verification API or use internal processes for verifying business credentials (e.g., require upload of business licenses, IDs). Mark traders as “Verified” once completed.

  • Trust & Safety Features: Add a verification badge in the Rolodex UI for verified buyers/sellers. Maintain an audit log of verifications and any compliance documents. If multiple geographies are involved, ensure the KYC process meets each region’s standards.

  • Compliance Tagging: Introduce compliance tags on listings or deals if needed - e.g., if a commodity trade is subject to export controls, tag it and alert ops to handle appropriately. Lay groundwork to automatically match compliance requirements based on commodity and country (perhaps link to a compliance database if available).

  • Deliverables: Verification process demo: a sample trader goes through KYC (can be a mocked flow) and gets a verified status in the system. Screenshot of a trader profile with a “Verified” label. A brief compliance report template showing what checks are done for a trade (for instance, “Checked against sanctions list: OK”). Reference document on KYC/AML policies applied.

Days 61-75: Automated Deal Matching Engine​

  • Matching Logic: Develop an engine to automatically match buyer requests with seller listings. For example, if a buyer needs 50 tons of a commodity and a seller has a listing that meets the commodity type and quantity, the system should flag this as a potential deal.

  • Algorithm Implementation: Start with straightforward criteria: exact commodity match, quantity available >= quantity requested, and maybe price overlap. Whenever a new listing or buy request is added, run the matching logic. Design it to handle partial matches too (e.g., one seller could fulfill part of a large buy order, or vice versa).

  • Notification of Matches: When a match is found, generate a draft deal entry connecting the buyer and seller. Notify the ops team on their dashboard and via WhatsApp (e.g., “Match found: Buyer A (wheat 100t) ↔ Seller B (wheat 200t)”). The ops team can then facilitate or approve initiating contact.

  • Deliverables: The matching engine output for various scenarios (documented test cases): one-to-one match, one-to-many (splitting an order), etc. Sample of a draft deal record created by the system, with fields like participants, commodity, proposed quantity and price. Logs or messages showing that the system automatically identified a match. Pseudocode or flowchart of the matching algorithm.

Days 76-90: Real-Time Quotation Tool​

  • Quotation Module: Build a tool for creating price quotations quickly. This could be a form where an ops user inputs buyer, seller, commodity, quantity, and the system fetches or suggests a price. Incorporate price calculation logic if applicable (e.g., pulling the latest market price as a baseline).

  • Template & Versioning: Design quote templates (including terms and conditions) that can be generated as PDF or sent as a message. Implement version control for quotes - each negotiation might have multiple quotes exchanged, so track revisions.

  • Integration with Messaging: Allow ops to send a quote to a buyer or seller directly via WhatsApp/Telegram from the system. For example, an ops user can click “Send Quote” and the buyer receives a formatted message or PDF of the quote through the bot.

  • Deliverables: A demonstration of creating a quote for a sample deal and the system generating a formatted output (PDF or message). Include the quoting formula or source of price (e.g., “using London Metal Exchange price for copper as reference”). If integrated with messaging, provide the content of a WhatsApp message showing a quote being delivered.

Days 91-105: Order Automation & Approval Workflow​

  • Deal Confirmation Workflow: Once a buyer and seller agree (possibly after negotiation and quote adjustments), automate the conversion of a “matched deal” into an official order. This includes finalizing quantity, price, and generating an order record with a unique ID.

  • Approval via Messaging: Implement manager approvals for deals above certain thresholds. For instance, send a WhatsApp message to a manager with deal details and options to approve or reject. Utilizing instant messaging accelerates decision-making for approvals. Parse the manager’s response (e.g., "Approve") to continue the workflow.

  • Confirmation Notifications: Upon approval, send out deal confirmation messages to both buyer and seller via WhatsApp/Telegram automatically. Include key order details and next steps (almost like an automated contract note). Ensure that these messages use the official API templates for transactional messages.

  • Deliverables: Example scenario: a high-value deal triggers an approval request to a manager’s WhatsApp, the manager replies “Approve”, and the system logs approval and notifies trading parties. Provide the message transcript for this scenario. Also present an “Order Confirmation” document or message that the system would send out once a deal is confirmed (with placeholders for signatures or formal acceptance if needed).

Days 106-120: WhatsApp/Telegram Workflow Automation​

  • ChatOps for Trade Negotiation: Expand bot capabilities to facilitate negotiation. For instance, create a dedicated WhatsApp group per deal with the buyer, seller, and the ops bot. The bot can post updates (like quote revisions) and both parties can negotiate terms in the chat, while the bot listens for keywords or explicit confirmations to trigger system updates.

  • Ops Commands via Chat: Enable the ops team to execute certain commands through WhatsApp/Telegram. E.g., an ops user could send “/listings rice” in Telegram to retrieve latest rice listings in chat, or “/status order123” to get the status of a particular order. This allows quick checks and updates on mobile without logging into the UI.

  • Ensuring Sync: Make sure that all chat-driven actions are mirrored in the system database. If a buyer says “Yes, confirmed at $X price” in chat, the ops should mark the deal as confirmed in the CRM (perhaps the bot could prompt ops to do so or auto-mark it). Provide a way to download or view the chat transcript in the CRM for audit.

  • Deliverables: Show a prototype negotiation via Telegram bot or WhatsApp where two parties agree on terms and the system captures the outcome. Provide a list of available bot commands for ops and examples of their usage. Include any bot flow charts or state diagrams created to handle multi-turn negotiations.

Days 121-135: Multi-Geo Support & Localization​

  • Internationalization (i18n): Modify the system to handle multiple languages and locales. Externalize all user-facing text for easy translation (including message templates for WhatsApp). If expanding to non-English speaking regions, prepare a plan to add translations for key languages.

  • Multi-Currency Handling: Add support for trading in different currencies. This includes storing currency along with prices and possibly integrating a currency conversion API for reference rates. Ensure all monetary values are clearly tagged with currency (USD, EUR, INR, etc.) to avoid confusion.

  • Regional Compliance: Introduce region-specific rules. For example, if trading agricultural commodities in Country A requires an export permit, the system should tag such deals and prompt ops to check permit status. Implement a compliance checklist that varies by country/region and commodity (this could tie into the compliance tagging system).

  • Deliverables: Demonstration of the app in at least one additional language (even if partially, e.g., the UI and one message template in Spanish). A test case showing a deal in EUR with conversion to USD for reporting. Documentation of region-based compliance rules (perhaps a table of regions vs. required checks/tags). Evidence that the system can gracefully switch locale (screenshots or config files).

Days 136-150: Operational CRM Dashboard & Integration​

  • Ops Dashboard: Build a unified dashboard for the operations team to monitor all trading activities. This should include: active deals in negotiation, pending approvals, recent matches made by the system, and alerts (like “KYC pending for Buyer X” or “Price updated by Seller Y”).

  • CRM Integration: Ensure every entity (contact, listing, deal, order) is linked in the CRM for a 360° view. For example, from a contact’s page, ops can see all deals and quotes involving that contact. This might involve some backend refactoring to link records and front-end work to display these relationships.

  • Workflow Tools: Add tools for ops to manage their workflow: to-do lists or task reminders (e.g., “Follow up with Buyer X tomorrow”), ability to assign an ops team member to a deal, and notes section on deals for internal comments.

  • Deliverables: Screenshot of the operational dashboard with dummy data indicating how information is organized (e.g., a section for “Matches waiting for approval”). Walkthrough of using the CRM: starting from a contact, navigate to their deals, then to an order. Include a sample internal note or task and how it’s created/displayed. Documentation of any additional data relationships added (like mapping an order back to a listing and contact).

Days 151-165: AI Insights & Predictive Analytics​

  • Predictive Deal Scoring: Employ machine learning to predict the likelihood of a deal closing successfully or flag deals at risk. Use historical deal data (if available) to train a model or create rules that score open deals (consider factors like response time, number of quote iterations, price competitiveness). This is similar to predictive lead scoring in sales, applied to trade deals.

  • Intelligent Alerts: Use AI to analyze patterns. For instance, if a certain commodity’s market price is volatile, alert if a deal is dragging (the system might suggest to hurry or hedge). If a buyer usually closes deals within 3 days and one is open for 7, highlight it as an outlier needing attention.

  • Recommendation Add-ons: Beyond matching exact needs, suggest related opportunities. E.g., “Buyer A often buys commodity X; there is a new seller listing commodity X - suggest checking that out.” Or if a seller’s listing is unmatched for a while, recommend possible price adjustment (using price data trends).

  • Deliverables: A report or UI element showing deal scores or risk levels for current open deals (with explanation for each score e.g., “70% chance to close - based on past pattern” or “At risk: buyer inactive for 5 days”). Any Jupyter notebooks or training code used to derive the model. Examples of intelligent alerts generated by the system (could be text lines like “Deal 45: price moved 5% against quote, consider revising quote.”).

Days 166-180: Final Testing, Deployment & Rollout​

  • End-to-End Testing: Test every user story: creating listings, matching, negotiating via chat, approvals, order creation, etc., under various conditions. Also test failure modes (e.g., what if WhatsApp message fails to send - does the system retry or alert?). Perform load testing on matching engine and messaging (simulate many messages at once).

  • Security & Data Protection: Review security one more time. Ensure that one client’s data cannot be seen by another (if multi-tenant). Check that all sensitive data (personal info, trade details) is transmitted and stored securely. If audit logs are needed for compliance, verify they’re recording the necessary info.

  • Production Deployment: Deploy the application to a production environment. Configure background schedulers (for any cron jobs like reminders), and monitoring/alerting for uptime. If needed, set up a separate environment for different regions (to comply with data residency laws).

  • User Training & Handover: Create user guides for the ops team and any external traders who might use a portal or receive communications. Provide training sessions or videos demonstrating common workflows (from creating a listing to closing a deal). Handover documentation to the IT/DevOps team for maintenance.

  • Deliverables: Test logs or summary highlighting that all critical scenarios passed (and how issues were addressed). A deployment report with environment details (server URLs, services, any third-party accounts configured like WhatsApp Business). Training material excerpts (slides or cheat-sheets for using the system). Sign-off from a hypothetical QA or ops lead that the system is ready for live usage.

Tech Stack and Tools​

  • Backend: Likely Node.js (Express or NestJS) for a scalable, event-driven backend to handle real-time matching and messaging. Python could be used for the AI components (e.g., Jupyter for predictive analytics). Leverage a message broker (RabbitMQ/Kafka) if needed to handle asynchronous matching jobs and message queueing.

  • Frontend: Angular or React for an enterprise-grade UI. Angular might be chosen for its built-in structure (as large teams often use it), with NgRx for state management (to handle real-time updates of deals/messages). Responsive design is key so ops can use tablets or smaller screens when needed.

  • Database: PostgreSQL for core data (traders, listings, orders) ensuring transactional integrity for trades. Elasticsearch for searching listings and contacts quickly (especially full-text search on commodity descriptions). Redis for caching recent matches or frequently accessed reference data (like currency rates).

  • Messaging Integration: Official WhatsApp Business Cloud API (using HTTP requests or an SDK as in Meta’s docs) and Telegram Bot API. No third-party hubs; manage webhooks and template messages in-house. Use the WhatsApp Cloud API for sending templated notifications (e.g., approvals) and Telegram’s API for command bots.

  • AI/ML & Data: Use Pandas/NumPy and scikit-learn or TensorFlow for predictive modeling on deal closure and pricing recommendations. If real-time price data is needed, integrate an external API (for commodity market prices) and possibly use Python scripts to ingest and analyze that data.

  • DevOps: Employ containerization (Docker) to allow deploying components (web app, bot service, matching engine) independently. Kubernetes could be used if expecting to scale across geographies. Monitoring via tools like Prometheus/Grafana to watch matching engine performance and message delivery success rates. CI/CD pipeline set up for automated testing and deployment (GitLab CI or GitHub Actions).