Alpaca Trading API Production Deployment - AI-Optimized Intelligence

Critical Configuration Settings

API Rate Limits

• Hard limit: 200 requests/minute for trading operations
• Production reality: Use maximum 180 requests/minute to leave buffer
• Failure mode: Complete lockout for 60 seconds when exceeded
• Impact: Positions bleed during market volatility when rebalancing needed

WebSocket Connection Management

• Connection stability: Drops occur every 2-3 hours
• Failure frequency: Increases during market volatility and VIX spikes
• Critical timing: Disconnections happen during worst market conditions
• Recovery requirement: Custom reconnection logic with exponential backoff (2^retry_count seconds, capped at 60s)

Memory Management for 24/7 Operations

• Baseline usage: 200MB initial
• Production growth: Reaches 2GB+ after 48 hours runtime
• Kill threshold: Restart containers before 80% memory usage
• Leak sources: WebSocket reconnection objects, historical data accumulation, pandas DataFrames

Resource Requirements

Capital Requirements

Capital Level	Constraints	Viability
< $25K	PDT rule: 3 day trades/week max	Development only
$25K-$50K	PDT compliant but limited diversification	Minimal viable
$50K+	Meaningful diversification possible	Production ready

Infrastructure Costs by Platform

Platform	Monthly Cost	Setup Complexity	Production Suitability	Critical Failure Mode
AWS Lambda	$5-50	Low	Scheduled rebalancing only	Cold starts during volatility
AWS ECS	$30-200	Medium	24/7 trading	Service discovery failures
DigitalOcean	$20-100	Low	Simple strategies	No managed databases
Kubernetes	$100-500	High	Enterprise only	Over-engineering complexity

Monitoring Stack Costs

• Basic monitoring: $50-100/month (Grafana + InfluxDB)
• Professional alerting: $20-50/month (PagerDuty)
• Total observability budget: $100-300/month for production systems

Critical Warnings

Paper Trading vs Live Trading Reality

• Performance degradation: Expect 50-80% worse results in live markets
• Fill quality difference: Paper assumes midpoint fills with zero slippage
• Slippage reality: 0.01-0.03% for liquid stocks, destroys strategies with <0.1% edge
• Timing discrepancy: Paper trading latency differs from live execution

Production Failure Scenarios

Rate Limit Death Spiral

• Trigger condition: Algorithm attempts to rebalance 20+ positions during market volatility
• Time to failure: Seconds during high VIX periods
• Consequence: Complete trading halt for 60+ seconds while positions deteriorate
• Prevention: Implement token bucket rate limiter with request queuing

WebSocket Disconnection During Critical Moments

• Failure pattern: Disconnections coincide with market stress events
• Data loss: Real-time market data and order status updates stop
• Detection time: Often discovered through PnL reports, not monitoring
• Mitigation: Exponential backoff reconnection + fallback REST API polling

Memory Leak System Death

• Timeline: Becomes critical after 48-72 hours continuous operation
• Symptom progression: 200MB → 2GB+ → system kill by OS
• Business impact: Trading halt during market hours
• Prevention: Proactive container restart at 80% memory threshold

Implementation Specifications

Rate Limiting Implementation

# Token bucket pattern with 180 req/min limit (buffer for 200 limit)
max_calls = 180
window = 60  # seconds
# Exponential backoff on limit breach
wait_time = window - (now - oldest_call) + 1

WebSocket Reconnection Logic

# Exponential backoff with cap
wait_time = min(2 ** retry_count, 60)  # Max 60 second wait
max_retries = 5  # Before manual intervention required

Circuit Breaker Thresholds

• Loss threshold: 1.0% account value in 30 minutes
• Action: Immediate algorithm shutdown and position freeze
• Override: Manual intervention required for restart

Database Schema Requirements

• Position tracking: Real-time symbol/quantity/avg_cost persistence
• Order state: Pending orders survive container restarts
• Trading signals: Historical decision tracking for post-mortem analysis

Decision Support Intelligence

When to Use Multiple Trading Bots vs Monolithic

• Multiple bots: One per strategy, isolated failure domains
• Monolithic: Shared state creates cascade failures during market stress
• Resource allocation: Each bot gets dedicated API rate limit bucket
• Debugging advantage: Strategy isolation prevents cross-contamination

Database Technology Decisions

Technology	Use Case	Why Not Others
PostgreSQL	Transactional data (positions, orders)	ACID compliance needed
Redis	Real-time market data caching	Memory speed required
SQLite	Never in production	Locks during writes, missed trades
MongoDB	Avoid for trading	Overkill complexity for structured data

Monitoring Alert Priorities

Critical (Immediate Action Required)

• WebSocket disconnected > 2 minutes
• Position size > 10% account value
• Daily loss > 2% account value
• API error rate > 10% over 5 minutes
• Memory usage > 85%

Warning (Check During Market Hours)

• Fill rate < 95% past hour
• Average slippage > 5 basis points
• Trading volume 3x normal
• Database connection failures

Platform-Specific Breaking Points

AWS Lambda Limitations

• Cold start latency: 500ms-2s during market volatility
• Execution timeout: 15 minute maximum
• Concurrent execution: Limited during scaling events
• Best use: Scheduled rebalancing, not real-time trading

Kubernetes Over-Engineering Risk

• Complexity cost: $100-500/month minimum
• Setup time: Weeks for proper configuration
• Operational overhead: Requires dedicated DevOps knowledge
• Simple alternative: Single-container deployment adequate for most strategies

Container Orchestration Requirements

# Resource limits prevent memory leak system death
resources:
  limits:
    memory: "2Gi"  # Hard kill before leak gets worse
    cpu: "1000m"
# Health checks detect WebSocket failures
livenessProbe:
  httpGet:
    path: /health
    port: 8080
  periodSeconds: 30

Operational Intelligence

Historical Failure Analysis

• March 2024 rate limit tightening: Strategies working in development threw 429 errors every 30 seconds in production
• February 2024 WebSocket issues: Multi-hour outages during trading hours
• Market stress periods: Algorithm failures clustered during high volatility events

Real-World Performance Degradation Factors

1. Bid-ask spread costs: 0.01-0.03% per trade side
2. Partial fill frequency: Increases during volatility
3. Market impact: Noticeable with positions >$100K
4. Slippage accumulation: Destroys strategies with thin margins

Disaster Recovery Time Requirements

• VPS failure: 5-15 minutes to failover to backup system
• Database corruption: 30-60 minutes to restore from backup
• Algorithm bug: Immediate circuit breaker activation required
• API outage: Manual broker access needed for emergency exits

Capital Efficiency Thresholds

• $10K account: Transaction costs consume 50%+ of profits
• $25K account: PDT rule compliance, limited strategy types
• $50K account: Meaningful diversification becomes possible
• $100K+ account: Professional-grade infrastructure justified

Technology Stack Recommendations

Production-Ready Architecture

• Application: Docker containers with health checks
• Database: PostgreSQL for state, Redis for caching
• Monitoring: Grafana + InfluxDB + Telegraf
• Alerting: PagerDuty or equivalent professional service
• Deployment: Docker Compose or managed container service

Development vs Production Differences

• Testing environment: Paper trading with artificial perfect conditions
• Production reality: Slippage, partial fills, API failures, network issues
• Resource scaling: 10x memory usage, 5x API call volume in production
• Monitoring complexity: Simple logging insufficient, structured metrics required

This intelligence extraction preserves all operational context while structuring information for AI decision-making and automated implementation guidance.