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crypto-portfolio-manager

Infosys Springboard 6.0 | Python Crypto Investment Manager Project

This repository hosts the source code for the Crypto Portfolio Manager, a system designed to calculate optimal crypto asset mixes and perform risk monitoring.

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Project Architecture

The system is built using a Decoupled Two-Tier Architecture, ensuring the front-end and back-end are independent and communicate securely via API calls.

Component	Technology	Role
Backend API	Python (FastAPI)	Handles all data processing, calculations, and MongoDB interactions.
Database	MongoDB Atlas	Stores user authentication and future risk trend data.
Frontend UI	React.js	Provides the user interface for input and display.
Security	JWT (JSON Web Tokens)	Secures the data pipeline between the Frontend and Backend API.

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🟢 MILESTONE 1 (Week 1 & 2): Setup and Verification Summary

Milestone 1 successfully established the project environment and verified all core technology integrations.

Milestone 1 Requirement	Technical Implementation	Status
Prepare Python with database	MongoDB Atlas connection verified and test user schema inserted.	COMPLETED
Teach parallel ways & math	Theoretical foundation established: Sharpe Ratio (Math) and Threading/Multiprocessing (Concurrency).	COMPLETED
Plan crypto types	Data Strategy defined: Uses historical data (Kaggle) for analysis and real-time data (CoinGecko API) for monitoring.	COMPLETED
End-to-End Integration	Full authentication pipeline (React Login $\rightarrow$ JWT $\rightarrow$ MongoDB) successfully tested and verified.	COMPLETED

🔵 MILESTONE 2: Module 1 - Investment Mix Calculator

Milestone 2 successfully implemented the core mathematical and concurrency engine. The system can now suggest a "Profitable Mix" based on historical risk/return analysis.

Milestone 2 Requirement	Technical Implementation	Status
Log Returns Calculation	Implemented log-normal return processing using NumPy and Pandas.	COMPLETED
Monte Carlo Engine	Built a parallelized engine running 10,000 simulations per request.	COMPLETED
Multiprocessing	Optimized performance using Python's Pool to utilize multi-core CPUs.	COMPLETED
Investment Strategy UI	New React interface for budget input and selection of 56+ unique assets.	COMPLETED
Data Persistence	Detailed profitable mixes and Sharpe ratios saved to MongoDB history.	COMPLETED

Key Optimization Logic: The Sharpe Ratio

The system calculates the Sharpe Ratio for every simulated portfolio: $$Sharpe Ratio = \frac{R_p - R_f}{\sigma_p}$$ Where $R_p$ is portfolio return, $R_f$ is risk-free rate, and $\sigma_p$ is portfolio volatility. The engine selects the weights that maximize this value.

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🟡 MILESTONE 3: Module 2 - Risk Monitoring & Alerts

The final module enables live tracking, professional file saving, and urgent risk alerts.

Milestone 3 Requirement	Technical Implementation	Status
Risk Checker	Uses parallel tasks to fetch live prices and apply status badges.	COMPLETED
Identity System	Persistent user login/signup with hashed password security.	COMPLETED
Predictor	Predicts profitable mixes from historical dataset.csv changes.	COMPLETED
Simple Database	Portfolio trends and removals are synced instantly to the cloud.	COMPLETED
File Saver	Generates clean, text-based CSV reports for Excel compatibility.	COMPLETED
Alert Link	Immediate email notifications for DANGER zone assets.	COMPLETED

Live Risk Logic

The Risk Engine evaluates assets using a percentage-based threshold system:

• 🟢 STABLE: Price increase > 5% since purchase.
• 🟡 WARNING: Price within +/- 5% of purchase price.
• 🔴 DANGER: Price drop > 5% (Triggers immediate email alert).

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🟣 MILESTONE 4 (Weeks 7-8): Module 3 - Rule Setter & Stress Testing (Final Release)

It introduces advanced user autonomy through dynamic rule setting ("Rule Based Mixing") and portfolio resilience testing ("Hard Situation" Simulator).

Milestone 4 Requirement	Technical Implementation	Status
Rule Setter Module	Created a dynamic constraint engine allowing users to mix Fixed Amounts ($) and Percentages (%) simultaneously.	COMPLETED
"Hard Situation" Tester	Implemented StrategyMixer.js to simulate different market conditions (Safe, Balanced, Risk) and adjust weights automatically.	COMPLETED
Constraint Logic	Algorithm processes fixed dollar allocations first, then distributes remaining capital proportionally based on percentage rules.	COMPLETED
Conflict Resolution	Built-in validation prevents over-allocation (>100% budget) and guides the user to use "Remaining" logic.	COMPLETED
Final UI Polish	Unified the entire application under the "Royal Blue & Gold" theme with responsive layouts and sticky footers.	COMPLETED

🧠 Core Logic: The Rule Engine The Rule-Based Mixer uses a Constraint Satisfaction Algorithm to generate portfolios:

• Priority 1 (Fixed Constraints): All rules defining a specific dollar amount (e.g., "$2000 in ETH") are deducted from the Total Budget first.
• Priority 2 (Percentage Constraints): Remaining budget is calculated. Specific percentage rules (e.g., "50% BTC") are applied to the remaining amount (or total, depending on user configuration).
• Priority 3 (The "Remaining" Bucket): Any capital left over after Priority 1 & 2 is swept into the specific asset defined as "Remaining" (e.g., USDT) to ensure 0% wasted capital.

🛡️ Core Logic: Stress Testing (Strategy Mixer) The system allows users to rebalance their existing portfolio based on market volatility predictions:

• 🛡️ SAFE Mode: Prioritizes Stablecoins (50%) and King Assets (BTC 25%) to preserve capital during crashes.
• ⚖️ BALANCED Mode: Shifts focus to Core L1s (ETH, SOL 40%) and BTC (30%) for steady growth.
• 🚀 RISK Mode: allocate heavily into High-Beta Alts (60%) and Core L1s (25%) for maximum aggressive growth during bull runs.

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Project Documentation

• Google Drive Documentation (Team Log)

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