Crypto Analytics Platform

Overview

Collaborated on a cryptocurrency analytics platform that combines quantitative scoring, real-time data processing, and algorithmic insights to evaluate digital assets. The system integrates a high-performance C# scoring engine with Python data pipelines to provide comprehensive market, fundamental, and technical analysis across multiple cryptocurrencies.

Built with a microservices architecture, the platform processes real-time market data via WebSocket connections and historical data through REST APIs, storing processed information in PostgreSQL with Redis caching for sub-second query performance.

System Architecture

High-Level Design

┌─────────────────────────────────────────────────────────────┐
│                     Data Ingestion Layer                    │
├─────────────────────────────────────────────────────────────┤
│  REST APIs (Historical)  │  WebSocket (Real-time)           │
│  - Binance               │  - Price feeds                   │
│  - CoinGecko             │  - Order book updates            │
│  - CryptoCompare         │  - Trade streams                 │
└──────────────┬───────────────────────────┬──────────────────┘
               │                           │
               ▼                           ▼
┌──────────────────────────┐  ┌──────────────────────────────┐
│   Python ETL Pipeline    │  │   C# Scoring Engine          │
│  - Data normalization    │  │  - Market analysis           │
│  - Feature engineering   │  │  - Fundamental scoring       │
│  - Time-series processing│  │  - Technical indicators      │
└──────────┬───────────────┘  └────────────┬─────────────────┘
           │                               │
           ▼                               ▼
┌─────────────────────────────────────────────────────────────┐
│                    Data Storage Layer                       │
├─────────────────────────────────────────────────────────────┤
│  PostgreSQL (Persistence)  │  Redis (Cache + Real-time)     │
│  - Historical data         │  - Live prices                 │
│  - Computed scores         │  - Session data                │
│  - Backtesting results     │  - Hot data cache              │
└─────────────────────────────────────────────────────────────┘

Quantitative Scoring Methodology

Composite Score Formula

The platform calculates a weighted composite score combining three analytical dimensions:

Composite Score:

S_composite = w₁·S_market + w₂·S_fundamental + w₃·S_technical

where:
  w₁ + w₂ + w₃ = 1 (weights sum to 1)
  S_market ∈ [0, 100]
  S_fundamental ∈ [0, 100]
  S_technical ∈ [0, 100]

Confidence Interval:

CI = 1 - (σ_scores / μ_scores)

where:
  σ_scores = standard deviation of component scores
  μ_scores = mean of component scores

Market Analysis Metrics

1. Liquidity Score

Bid-Ask Spread:

Spread% = ((P_ask - P_bid) / P_bid) × 100

where:
  P_ask = best ask price
  P_bid = best bid price

Market Depth:

Depth(n) = Σ(i=1 to n) (V_bid,i + V_ask,i)

where:
  V_bid,i = volume at i-th bid level
  V_ask,i = volume at i-th ask level
  n = number of order book levels

Slippage Estimation:

Slippage(Q) = (P_execution - P_mid) / P_mid

where:
  P_execution = average execution price for quantity Q
  P_mid = (P_bid + P_ask) / 2

2. Volume Profile Analysis

Volume-Weighted Average Price (VWAP):

VWAP = Σ(P_i × V_i) / Σ(V_i)

where:
  P_i = price at time i
  V_i = volume at time i

Volume Distribution:

VD(p) = V_total(p) / V_total

where:
  V_total(p) = total volume traded at price level p
  V_total = total volume across all price levels

Fundamental Analysis Metrics

1. On-Chain Metrics

Network Value to Transactions (NVT) Ratio:

NVT = Market_Cap / Daily_Transaction_Volume

Interpretation:
  NVT < 50  → Undervalued
  NVT > 100 → Overvalued

Market Value to Realized Value (MVRV) Ratio:

MVRV = Market_Cap / Realized_Cap

where:
  Realized_Cap = Σ(UTXO_i × Price_at_creation_i)
  
Interpretation:
  MVRV > 3.5 → Potential top
  MVRV < 1.0 → Potential bottom

Active Address Momentum:

AAM = (AA_current - AA_avg) / σ_AA

where:
  AA_current = current active addresses
  AA_avg = 30-day moving average
  σ_AA = standard deviation of active addresses

2. Tokenomics Scoring

Supply Inflation Rate:

IR = (S_current - S_previous) / S_previous × 100

where:
  S_current = current circulating supply
  S_previous = supply 1 year ago

Holder Concentration (Gini Coefficient):

G = (Σ(i=1 to n) Σ(j=1 to n) |x_i - x_j|) / (2n²μ)

where:
  x_i = holdings of address i
  μ = mean holdings
  G ∈ [0, 1] (0 = perfect equality, 1 = perfect inequality)

Technical Analysis Indicators

1. Momentum Indicators

Relative Strength Index (RSI):

RSI = 100 - (100 / (1 + RS))

where:
  RS = Average_Gain / Average_Loss
  Average_Gain = Σ(Gains over n periods) / n
  Average_Loss = Σ(Losses over n periods) / n
  
Interpretation:
  RSI > 70 → Overbought
  RSI < 30 → Oversold

Moving Average Convergence Divergence (MACD):

MACD = EMA₁₂ - EMA₂₆
Signal = EMA₉(MACD)
Histogram = MACD - Signal

where:
  EMA_n = Exponential Moving Average with period n
  EMA_t = α·P_t + (1-α)·EMA_(t-1)
  α = 2/(n+1) (smoothing factor)

2. Volatility Indicators

Bollinger Bands:

BB_middle = SMA_n(P)
BB_upper = BB_middle + k·σ_n
BB_lower = BB_middle - k·σ_n

where:
  SMA_n = Simple Moving Average with period n
  σ_n = standard deviation over n periods
  k = 2 (typically)
  
%B = (P - BB_lower) / (BB_upper - BB_lower)

Average True Range (ATR):

TR = max(H - L, |H - C_prev|, |L - C_prev|)
ATR = (ATR_prev × (n-1) + TR) / n

where:
  H = current high
  L = current low
  C_prev = previous close
  n = period (typically 14)

3. Trend Indicators

Exponential Moving Average (EMA):

EMA_t = α·P_t + (1-α)·EMA_(t-1)

where:
  α = 2/(n+1)
  P_t = price at time t
  n = period

Parabolic SAR:

SAR_t = SAR_(t-1) + AF × (EP - SAR_(t-1))

where:
  AF = Acceleration Factor (starts at 0.02, max 0.20)
  EP = Extreme Point (highest high or lowest low)

Statistical Models & Risk Metrics

Return Analysis

Log Returns:

r_t = ln(P_t / P_(t-1))

Properties:
  - Time-additive: r_(t,t+n) = Σ(i=t to t+n) r_i
  - Approximately normal for small returns

Volatility (Standard Deviation):

σ = √(Σ(r_i - μ)² / (n-1))

where:
  μ = mean return
  n = number of observations
  
Annualized: σ_annual = σ_daily × √252

Risk-Adjusted Performance Metrics

Sharpe Ratio:

SR = (R_p - R_f) / σ_p

where:
  R_p = portfolio return
  R_f = risk-free rate
  σ_p = portfolio standard deviation
  
Interpretation:
  SR > 1.0 → Good
  SR > 2.0 → Very good
  SR > 3.0 → Excellent

Sortino Ratio:

Sortino = (R_p - R_target) / σ_downside

where:
  R_target = target return
  σ_downside = √(Σ(min(0, r_i - R_target)²) / n)
  
Advantage: Only penalizes downside volatility

Maximum Drawdown:

MDD = max(DD_t) for all t

where:
  DD_t = (Peak_t - Trough_t) / Peak_t
  Peak_t = max(P_i) for i ≤ t
  Trough_t = min(P_i) for Peak_t ≤ i ≤ t

Calmar Ratio:

Calmar = Annual_Return / |MDD|

Interpretation:
  Higher is better (return per unit of drawdown risk)

Correlation & Covariance

Pearson Correlation:

ρ(X,Y) = Cov(X,Y) / (σ_X × σ_Y)

where:
  Cov(X,Y) = E[(X - μ_X)(Y - μ_Y)]
  ρ ∈ [-1, 1]

Beta (Market Sensitivity):

β = Cov(R_asset, R_market) / Var(R_market)

Interpretation:
  β = 1 → Moves with market
  β > 1 → More volatile than market
  β < 1 → Less volatile than market

Backtesting Framework

Walk-Forward Optimization

Process:

1. Split data: Training (T) | Validation (V) | Out-of-sample (O)
2. Optimize parameters on T
3. Validate on V
4. Test on O
5. Roll window forward
6. Repeat

Window sizes:
  Training: 60% of total period
  Validation: 20% of total period
  Out-of-sample: 20% of total period

Monte Carlo Simulation

Price Path Simulation:

S_t = S_(t-1) × exp((μ - σ²/2)Δt + σ√Δt × Z)

where:
  S_t = price at time t
  μ = drift (expected return)
  σ = volatility
  Δt = time step
  Z ~ N(0,1) (standard normal)

Value at Risk (VaR):

VaR_α = -Quantile_α(Returns)

where:
  α = confidence level (e.g., 0.95, 0.99)
  
Example: VaR_0.95 = $10,000
  → 95% confidence that loss won't exceed $10,000

Conditional Value at Risk (CVaR):

CVaR_α = E[Loss | Loss > VaR_α]

Advantage: Captures tail risk beyond VaR

Feature Engineering

Time-Series Features

Lag Features:

X_t-1, X_t-2, ..., X_t-n

Creates temporal dependencies

Rolling Statistics:

μ_rolling(n) = (1/n) × Σ(i=t-n+1 to t) X_i
σ_rolling(n) = √((1/n) × Σ(i=t-n+1 to t) (X_i - μ)²)

Rate of Change:

ROC_n = (P_t - P_(t-n)) / P_(t-n) × 100

Fourier Transform for Cyclical Patterns

Discrete Fourier Transform:

X_k = Σ(n=0 to N-1) x_n × e^(-2πikn/N)

where:
  k = frequency component
  N = number of samples
  
Identifies dominant cycles in price data

Data Pipeline Architecture

ETL Process Flow

Extract:

REST API polling (1-minute intervals)
WebSocket streaming (real-time)
Rate limiting: 1200 requests/minute
Retry logic: Exponential backoff

Transform:

Data normalization (z-score, min-max)
Missing value imputation (forward-fill, interpolation)
Outlier detection (IQR method, z-score > 3)
Feature scaling for ML models

Load:

Batch inserts (PostgreSQL): 10,000 records/batch
Stream updates (Redis): Sub-millisecond latency
Data partitioning: By symbol and date range
Indexing strategy: B-tree on (symbol, timestamp)

Database Optimization

PostgreSQL Time-Series Optimization:

-- Partitioning by date range
CREATE TABLE price_data_2024_01 PARTITION OF price_data
  FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

-- Hypertable for time-series (TimescaleDB)
SELECT create_hypertable('price_data', 'timestamp');

-- Continuous aggregates
CREATE MATERIALIZED VIEW hourly_ohlcv AS
  SELECT 
    time_bucket('1 hour', timestamp) AS hour,
    symbol,
    first(price, timestamp) AS open,
    max(price) AS high,
    min(price) AS low,
    last(price, timestamp) AS close,
    sum(volume) AS volume
  FROM price_data
  GROUP BY hour, symbol;

Redis Caching Strategy:

Hot data: TTL = 5 minutes
Warm data: TTL = 1 hour
Cold data: Eviction policy = LRU
Memory limit: 4GB with maxmemory-policy

Performance Metrics

System Benchmarks

Metric	Value	Method
Asset Processing	<5ms	Parallel execution (10 threads)
Batch Scoring (100)	<500ms	Async/await pattern
WebSocket Latency	<50ms	Direct TCP connection
DB Query (cached)	<10ms	Redis in-memory cache
DB Query (cold)	<100ms	Indexed PostgreSQL
ETL Throughput	10K+ records/s	Bulk inserts, vectorized ops

Scalability Characteristics

Horizontal Scaling:

Stateless microservices
Load balancing: Round-robin
Database replication: Master-slave
Cache distribution: Redis Cluster

Vertical Scaling:

CPU: 16 cores for parallel processing
RAM: 32GB for in-memory operations
Storage: SSD for low-latency I/O

Technologies Demonstrated

This project showcases expertise in:

Quantitative Finance - Statistical modeling, risk metrics, portfolio theory
Data Engineering - ETL pipelines, time-series databases, real-time streaming
Backend Development - C# / .NET 8, Python 3.11+, microservices
Database Design - PostgreSQL optimization, Redis caching, partitioning
Performance Engineering - Parallel processing, async programming, vectorization
Financial Mathematics - Stochastic calculus, Monte Carlo methods, option pricing theory

License

Private Project - Proprietary

Status: Active Development | Architecture: Microservices | Deployment: Cloud-based