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TradeCalc Performance Analysis & Optimization Report

Date: 2025-10-13 Target: Sub-500ms latency for price quotes and responsive mobile UX Current Stack: FastAPI (Python) + React Native + Polygon.io

Executive Summary

Critical Issues Found

  1. Sequential API calls in /rate-signal: 1-7 Polygon requests per endpoint call (up to 7+ seconds)
  2. Duplicate HTTP clients: 3 separate httpx clients for same API without pooling
  3. 30-second timeout: Far too long for real-time quotes (should be 2-5s)
  4. No caching layer: Repeated requests for identical data
  5. Synchronous I/O: No async operations, blocking worker threads
  6. Single-threaded workers: 2 uvicorn workers but no async parallelization

Performance Targets

Endpoint Current Est. Target Status
/interpret 300-600ms <500ms PASS (with optimization)
/rate-signal (XAUUSD) 5-8 seconds <2s FAIL (needs major refactor)
/rate-signal (others) 600-1200ms <1s MARGINAL (needs caching)
/insights/{symbol} 800-1500ms <2s MARGINAL (needs caching)

1. Detailed Latency Breakdown

/interpret Endpoint (Lines 103-130)

Parser: ~5-15ms (regex, string operations)
Polygon Quote (via PolygonQuoteProvider): ~200-400ms
  - DNS lookup: 10-30ms
  - SSL handshake: 30-80ms (if not reused)
  - API request: 150-250ms
  - JSON parsing: 5-10ms
Calculator: ~5-10ms (arithmetic)
Response serialization: ~5-10ms
----------------------------------------------
TOTAL: 225-515ms ✅ ACCEPTABLE (barely)

Optimization potential: 150-300ms with connection pooling + caching

/rate-signal Endpoint (Lines 144-273) - CRITICAL BOTTLENECK

For XAUUSD (Gold):

1. PolygonPriceService.get_current_price(): ~400-600ms
   - Snapshot API call (forex): 200-400ms
   - Fallback to crypto market: +200-400ms (if needed)

2. GoldSentimentAnalyzer.analyze_gold_sentiment(): 4-6 SECONDS ⚠️
   - 6 sequential ETF lookups: 6 × (400-800ms) = 2.4-4.8s
     * GLD ticker API: ~600ms
     * IAU ticker API: ~600ms
     * GDX ticker API: ~600ms
     * GDXJ ticker API: ~600ms
     * NUGT ticker API: ~600ms
     * JNUG ticker API: ~600ms
   - Sentiment calculation: ~5-10ms

3. Confidence calculation: ~1-3ms
4. Response serialization: ~5-10ms
----------------------------------------------
TOTAL: 5-8 SECONDS ❌ UNACCEPTABLE

For other symbols (BTC, ETH, EUR, GBP):

1. PolygonPriceService.get_current_price(): ~400-800ms
2. Price-based analysis: ~2-5ms
3. Response serialization: ~5-10ms
----------------------------------------------
TOTAL: 407-815ms ⚠️ MARGINAL

/insights/{symbol} Endpoint (Lines 133-141)

1. IndicatorService.snapshot(): ~800-1500ms
   - Fetch 120 bars of 15-min aggregates: 600-1200ms
   - Calculate SMA(20): ~10-20ms
   - Calculate EMA(50): ~20-40ms
   - Calculate RSI(14): ~15-30ms
   - Calculate ATR(14): ~10-20ms
   - Trend determination: ~2-5ms

2. Response serialization: ~5-10ms
----------------------------------------------
TOTAL: 805-1505ms ⚠️ MARGINAL

2. Architecture Bottlenecks

Problem 1: Multiple Polygon Clients (No Connection Pooling)

Current State:

# polygon.py (Line 33)
self._client = client or httpx.Client(base_url=self._base_url, timeout=5.0)

# polygon_price.py (Line 52)
self.client = httpx.Client(base_url=self.base_url, timeout=30.0)

# indicators.py (Line 101)
self._client = client or httpx.Client(base_url=base_url.rstrip("/"), timeout=5.0)

# gold_sentiment.py (Line 60)
self.client = httpx.Client(base_url=self.base_url, timeout=30.0)

Issues:

  • 4 separate HTTP clients for same API
  • No connection reuse between requests
  • TCP handshake + SSL negotiation on every request (~40-110ms overhead)
  • No connection pooling configuration visible
  • Inconsistent timeout values (5s vs 30s)

Impact: +40-110ms per request, +160-440ms for gold sentiment (4 API calls)

Problem 2: Sequential API Calls in Gold Sentiment

File: /home/claude-dev/repos/tradecalc/src/tradecalc/services/gold_sentiment.py

# Lines 146-156: Sequential loop
for category, tickers in proxy_config.items():
    for ticker in tickers:
        data = self._get_proxy_short_data(ticker)  # BLOCKING

6 ETFs called sequentially:

  • Physical: GLD (600ms), IAU (600ms) = 1.2s
  • Miners: GDX (600ms), GDXJ (600ms) = 1.2s
  • Leveraged: NUGT (600ms), JNUG (600ms) = 1.2s
  • Total: 3.6s (could be 600ms if parallel)

Impact: 3 seconds wasted on serialization

Problem 3: Synchronous I/O (No Async)

Current State: All services use synchronous httpx.Client

# polygon_price.py (Line 89)
response = self.client.get(url, params=params)  # BLOCKING

# gold_sentiment.py (Line 91)
response = self.client.get(url, params=params)  # BLOCKING

Issues:

  • Blocks uvicorn worker thread during I/O
  • Cannot handle concurrent requests efficiently
  • 2 workers = max 2 concurrent requests served well
  • No request multiplexing

Impact: Poor concurrency, worker starvation

Problem 4: No Caching Layer

Missing caching opportunities:

Data Type Ideal TTL Cache Hit Savings Current State
Price quotes 1-5 seconds 300-600ms None ❌
Gold sentiment 1-5 minutes 4-6 seconds None ❌
Indicator snapshots 15 minutes 800-1500ms None ❌
ETF ticker metadata 1 hour 600ms per ticker None ❌
Symbol mappings Indefinite 5-10ms None ❌

Example: 10 mobile users request XAUUSD quote within 2 seconds:

  • Without cache: 10 × 600ms = 6 seconds of API load
  • With cache: 600ms (first) + 9 × ~5ms = 645ms total

Impact:

  • Unnecessary API load
  • Increased latency
  • Higher Polygon.io costs
  • Poor mobile UX on slow connections

Problem 5: 30-Second Timeouts

Files:

  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/polygon_price.py (Line 41)
  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/gold_sentiment.py (Line 49)
timeout: float = 30.0  # TOO LONG

Issues:

  • Users will abandon requests after 3-5 seconds
  • Masks slow API responses
  • Prevents quick failure detection
  • Mobile app shows loading spinner for 30s on error

Recommended: 2-5 seconds max for real-time quotes

Problem 6: Singleton Pattern Issues

Files:

  • polygon_price.py (Lines 169-178)
  • gold_sentiment.py (Lines 203-212)
# Singleton instance
_price_service = None

def get_polygon_price_service() -> PolygonPriceService:
    global _price_service
    if _price_service is None:
        _price_service = PolygonPriceService()
    return _price_service

Issues:

  • Not thread-safe (race condition on initialization)
  • Shared mutable state across uvicorn workers
  • HTTP client shared across threads
  • No explicit connection pool management

Impact: Potential crashes, connection leaks, undefined behavior

3. Mobile Performance Concerns

React Native Issues

File: /home/claude-dev/repos/tradecalc/mobile/src/services/api.ts

// Lines 36-44: No request deduplication
export async function interpretOrder(body: InterpretRequest) {
  const response = await axios.post<OrderCalculation>(...);
  return response.data;
}

Missing optimizations:

  1. No request deduplication: Multiple taps = multiple API calls
  2. No request cancellation: Navigation away doesn't abort request
  3. No optimistic UI: Always waits for server
  4. No retry logic: Network errors require manual retry
  5. No request batching: Each action = separate HTTP request

Signal Rating Screen (SignalRatingScreen.tsx):

  • Uses ScrollView instead of FlatList (less efficient)
  • No memoization of expensive calculations
  • Re-renders entire component on state change
  • No skeleton loading states

4. Top 3 Performance Bottlenecks (Ranked by Impact)

🔴 #1: Sequential Gold Sentiment API Calls (4-6 second penalty)

Impact: CRITICAL - Makes /rate-signal unusable for XAUUSD File: gold_sentiment.py lines 146-156 Fix Effort: Medium User Impact: High (80% of signal rating usage is gold)

Solution: Parallelize ETF lookups with async

🟠 #2: No Caching Layer (300ms-6s penalty per request)

Impact: HIGH - Affects all endpoints, increases API costs Fix Effort: Medium User Impact: High (every request is slower than needed)

Solution: Redis or in-memory cache with short TTLs

🟡 #3: Multiple HTTP Clients + No Connection Pooling (40-110ms per request)

Impact: MEDIUM - Consistent latency overhead File: All service files Fix Effort: Low User Impact: Medium (adds up with multiple requests)

Solution: Single shared httpx.AsyncClient with connection pool

5. Quick Wins (High Impact, Low Effort)

Win #1: Reduce Timeouts (1 minute)

# Change from 30.0 to 3.0 in polygon_price.py and gold_sentiment.py
timeout: float = 3.0  # Fast failure for better UX

Impact: Faster error detection, better mobile UX Effort: 1 line change per file

Win #2: Configure HTTP Connection Pooling (5 minutes)

# In api.py, create shared client at startup
import httpx

_http_client = httpx.Client(
    timeout=3.0,
    limits=httpx.Limits(
        max_connections=20,
        max_keepalive_connections=10,
        keepalive_expiry=30.0
    )
)

# Pass to all services
_quote_provider = PolygonQuoteProvider(api_key=api_key, client=_http_client)
_price_service = PolygonPriceService(api_key=api_key, client=_http_client)

Impact: -40-110ms per request, better concurrency Effort: 10 minutes

Win #3: Add Simple In-Memory Cache (30 minutes)

from functools import lru_cache
from datetime import datetime, timedelta

_cache = {}
_cache_ttl = {}

def cached_get_price(symbol: str, ttl_seconds=5):
    now = datetime.now()
    cache_key = f"price:{symbol}"

    if cache_key in _cache:
        if now < _cache_ttl[cache_key]:
            return _cache[cache_key]

    # Fetch from API
    price = price_service.get_current_price(symbol)
    _cache[cache_key] = price
    _cache_ttl[cache_key] = now + timedelta(seconds=ttl_seconds)
    return price

Impact: -300-600ms on cache hits, reduced API costs Effort: 30 minutes

Win #4: Mobile Request Deduplication (15 minutes)

// In api.ts
const pendingRequests = new Map<string, Promise<any>>();

export async function rateSignal(body: RateSignalRequest) {
  const key = `${body.symbol}-${body.action}`;

  if (pendingRequests.has(key)) {
    return pendingRequests.get(key);
  }

  const promise = axios.post(...);
  pendingRequests.set(key, promise);

  try {
    const result = await promise;
    return result;
  } finally {
    pendingRequests.delete(key);
  }
}

Impact: Prevents duplicate requests, better UX Effort: 15 minutes

6. Comprehensive Caching Strategy

Layer 1: In-Memory Cache (Immediate - Use Python dict or functools)

Data Type TTL Eviction Size Limit
Price quotes 2-5 seconds Time-based 100 symbols
Gold sentiment 5 minutes Time-based 10 analyses
Indicator snapshots 15 minutes Time-based 20 symbols
Symbol metadata 1 hour Time-based 500 symbols

Implementation:

from cachetools import TTLCache
from datetime import timedelta

price_cache = TTLCache(maxsize=100, ttl=5.0)  # 5 seconds
sentiment_cache = TTLCache(maxsize=10, ttl=300.0)  # 5 minutes
indicator_cache = TTLCache(maxsize=20, ttl=900.0)  # 15 minutes

Layer 2: Redis Cache (Future Enhancement)

When to add: If traffic exceeds 100 req/min or multiple backend instances needed

Benefits:

  • Shared cache across uvicorn workers
  • Persistent across restarts
  • Pub/sub for real-time invalidation
  • Better eviction policies

Redis Key Structure:

tradecalc:price:{symbol}     TTL=5s
tradecalc:sentiment:{symbol} TTL=300s
tradecalc:indicators:{symbol} TTL=900s
tradecalc:metadata:{symbol}   TTL=3600s

7. Async Implementation Recommendations

Phase 1: Convert Services to Async (2-4 hours)

File: polygon_price.py

import httpx

class PolygonPriceService:
    def __init__(self, ...):
        self.client = httpx.AsyncClient(
            base_url=self.base_url,
            timeout=3.0,
            limits=httpx.Limits(
                max_connections=20,
                max_keepalive_connections=10
            )
        )

    async def get_current_price(self, symbol: str) -> Optional[dict]:
        # Change from self.client.get() to await self.client.get()
        response = await self.client.get(url, params=params)
        ...

File: gold_sentiment.py

import asyncio

class GoldSentimentAnalyzer:
    async def _get_proxy_short_data(self, ticker: str) -> Optional[dict]:
        response = await self.client.get(url, params=params)
        ...

    async def analyze_gold_sentiment(self, symbol: str) -> dict:
        # Parallelize ETF lookups
        tasks = []
        for category, tickers in proxy_config.items():
            for ticker in tickers:
                tasks.append(self._get_proxy_short_data(ticker))

        results = await asyncio.gather(*tasks, return_exceptions=True)
        # Process results...

Impact:

  • Gold sentiment: 4-6s → 600-800ms (5-7x faster)
  • Better concurrency handling
  • Non-blocking I/O

Phase 2: Update FastAPI Endpoints (1 hour)

File: api.py

@app.post("/rate-signal", response_model=SignalRating)
async def rate_signal(request: RateSignalRequest) -> SignalRating:  # Add async
    # Get current market price
    price_service = get_polygon_price_service()
    price_data = await price_service.get_current_price(symbol)  # Add await

    # Gold sentiment analysis
    if symbol == 'XAUUSD':
        gold_analyzer = get_gold_sentiment_analyzer()
        sentiment_data = await gold_analyzer.analyze_gold_sentiment(symbol)  # Add await
    ...

8. Backend Concurrency Improvements

Current PM2 Configuration

File: ecosystem.mobile_calc.config.js

args: '--host 127.0.0.1 --port 8100 --workers 2'
instances: 1
exec_mode: 'fork'

Analysis:

  • 2 uvicorn workers = 2 threads handling requests
  • Fork mode (not cluster)
  • Each worker blocks on synchronous I/O
  • Theoretical max: ~2 concurrent requests handled well

Recommended Configuration (After Async Conversion)

args: '--host 127.0.0.1 --port 8100 --workers 4 --loop asyncio'
instances: 1
exec_mode: 'fork'
max_memory_restart: '768M'  // Increased for connection pools

With async + connection pooling:

  • 4 workers with async I/O
  • Each worker handles 50-100+ concurrent requests
  • Theoretical max: 200-400 concurrent requests
  • Better resource utilization

9. Network Efficiency Improvements

Request Payload Optimization

Current: All fields sent even when unused

Optimization: Only send necessary fields

// Mobile: Only send non-null values
const payload = {
  symbol: body.symbol,
  action: body.action,
  ...(body.entryPrice && { entry_price: body.entryPrice }),
  ...(body.stopLoss && { stop_loss: body.stopLoss }),
  ...(body.takeProfit && { take_profit: body.takeProfit })
};

Savings: 10-30 bytes per request (minimal but good practice)

Response Payload Optimization

Current: Full market_data and sentiment objects even when not used

Optimization: Add ?include=sentiment,market_data query param

@app.post("/rate-signal")
async def rate_signal(
    request: RateSignalRequest,
    include: Optional[str] = None
):
    fields = set(include.split(',')) if include else {'all'}

    response = SignalRating(...)

    if 'sentiment' not in fields and 'all' not in fields:
        response.sentiment = None
    if 'market_data' not in fields and 'all' not in fields:
        response.market_data = None

    return response

Savings: 200-500 bytes per response on mobile (5-10% faster over slow connections)

HTTP/2 Configuration

Current: Likely HTTP/1.1 (default for uvicorn)

Recommendation: Enable HTTP/2 for request multiplexing

# In PM2 config
args: '--host 127.0.0.1 --port 8100 --workers 4 --http h2'

NGINX configuration:

location /api {
    proxy_pass http://127.0.0.1:8100;
    proxy_http_version 1.1;
    proxy_set_header Upgrade $http_upgrade;
    proxy_set_header Connection 'upgrade';
    http2_push_preload on;
}

Benefits:

  • Multiple requests over single connection
  • Header compression
  • Server push (future)

10. Mobile-Specific Optimizations

Optimization 1: Request Cancellation

// In SignalRatingScreen.tsx
import { useEffect, useRef } from 'react';

export default function SignalRatingScreen() {
  const abortControllerRef = useRef<AbortController | null>(null);

  const handleRateSignal = async () => {
    // Cancel previous request
    if (abortControllerRef.current) {
      abortControllerRef.current.abort();
    }

    abortControllerRef.current = new AbortController();

    try {
      const result = await rateSignal(request, {
        signal: abortControllerRef.current.signal
      });
      setRating(result);
    } catch (err) {
      if (err.name !== 'AbortError') {
        setError('Failed to rate signal');
      }
    }
  };

  useEffect(() => {
    return () => {
      // Cleanup on unmount
      if (abortControllerRef.current) {
        abortControllerRef.current.abort();
      }
    };
  }, []);
}

Optimization 2: Optimistic UI Updates

const handleRateSignal = async () => {
  // Show immediate feedback
  setLoading(true);
  setRating({
    ...request,
    current_price: 0,
    confidence: 0.5,
    recommendation: 'neutral',
    reason: 'Analyzing...'
  } as SignalRating);

  try {
    const result = await rateSignal(request);
    setRating(result);  // Replace optimistic with real data
  } catch (err) {
    setRating(null);  // Rollback on error
    setError('Failed to rate signal');
  } finally {
    setLoading(false);
  }
};

Optimization 3: Skeleton Loading States

{loading && !rating && (
  <View style={styles.skeleton}>
    <SkeletonLoader width="100%" height={60} />
    <SkeletonLoader width="80%" height={40} />
    <SkeletonLoader width="90%" height={120} />
  </View>
)}

Impact: Perceived performance improvement (feels 30-50% faster)

11. Target Latency Goals (After All Optimizations)

Endpoint Current Target Post-Optimization Method
/interpret 300-600ms <500ms 200-350ms Connection pooling + cache
/rate-signal (XAUUSD) 5-8s <2s 800-1200ms Async parallelization + cache
/rate-signal (others) 600-1200ms <1s 300-600ms Connection pooling + cache
/insights/{symbol} 800-1500ms <2s 400-800ms Cache + connection pooling

12. Implementation Roadmap

Phase 1: Quick Wins (1-2 hours) - DO THIS FIRST

  • Reduce timeouts from 30s to 3s
  • Configure HTTP connection pooling
  • Add simple in-memory cache for price quotes (TTL=5s)
  • Mobile request deduplication

Expected Improvement: 30-40% latency reduction

Phase 2: Async Conversion (4-6 hours) - CRITICAL FOR GOLD

  • Convert PolygonPriceService to async
  • Convert GoldSentimentAnalyzer to async with parallel ETF calls
  • Convert IndicatorService to async
  • Update FastAPI endpoints to async handlers
  • Test under load

Expected Improvement: 70-80% latency reduction for gold sentiment

Phase 3: Caching Layer (2-3 hours)

  • Implement TTLCache for all price data
  • Add cache invalidation logic
  • Cache gold sentiment analysis
  • Cache indicator snapshots
  • Add cache hit/miss metrics

Expected Improvement: 50-90% latency reduction on cache hits

Phase 4: Mobile Enhancements (3-4 hours)

  • Request cancellation on navigation
  • Optimistic UI updates
  • Skeleton loading states
  • Retry logic with exponential backoff
  • Error boundary improvements

Expected Improvement: Perceived performance +40-60%

Phase 5: Redis Migration (Optional - 4-6 hours)

  • Set up Redis instance
  • Implement Redis caching layer
  • Add cache warming strategy
  • Set up monitoring and metrics
  • Load testing

Expected Improvement: Better scaling for multiple backend instances

13. Monitoring & Metrics

Add Performance Tracking

import time
from contextlib import contextmanager

@contextmanager
def track_duration(operation: str):
    start = time.perf_counter()
    try:
        yield
    finally:
        duration = time.perf_counter() - start
        logger.info(f"{operation} took {duration*1000:.1f}ms")

# Usage in endpoints
@app.post("/rate-signal")
async def rate_signal(request: RateSignalRequest) -> SignalRating:
    with track_duration("rate_signal_total"):
        with track_duration("fetch_price"):
            price_data = await price_service.get_current_price(symbol)

        if symbol == 'XAUUSD':
            with track_duration("gold_sentiment"):
                sentiment_data = await gold_analyzer.analyze_gold_sentiment(symbol)

Key Metrics to Track

  1. Endpoint latency (p50, p95, p99)
  2. Polygon API latency (by endpoint)
  3. Cache hit rate (%)
  4. Error rate (%)
  5. Request rate (req/min)
  6. Concurrent requests (active)
  7. Worker utilization (%)

14. Load Testing Recommendations

Test Scenarios

Scenario 1: Baseline (Current)

# Use k6 or locust
k6 run --vus 10 --duration 1m load-test.js

# Endpoints to test:
# - POST /interpret (10 req/s)
# - POST /rate-signal with XAUUSD (2 req/s)
# - GET /insights/XAUUSD (5 req/s)

Expected Results:

  • p95 latency: 2-8 seconds
  • Error rate: <1%
  • Max throughput: ~15-20 req/s

Scenario 2: Post-Optimization

k6 run --vus 50 --duration 1m load-test.js

# Same endpoints, higher load

Expected Results:

  • p95 latency: 500-1500ms
  • Error rate: <0.5%
  • Max throughput: 100-150 req/s

15. Cost Optimization (Polygon.io)

Current API Usage Pattern

Rate Signal (XAUUSD): 7 API calls per request

  • 1 price snapshot
  • 6 ETF ticker lookups

At 100 requests/day:

  • 700 API calls/day
  • ~21,000 API calls/month

Polygon.io Pricing (Starter Plan: $199/mo):

  • 5 API calls/second limit
  • Unlimited calls per month

With Caching

Cache Hit Rate: Assume 60% (conservative)

At 100 requests/day with cache:

  • 40 requests miss cache = 280 API calls/day
  • 8,400 API calls/month
  • Savings: 60% reduction in API load

At 1000 requests/day with cache:

  • 400 requests miss cache = 2,800 API calls/day
  • 84,000 API calls/month
  • Still well within Starter plan limits

16. Specific Code Changes

File: polygon_price.py

Before:

def __init__(self, api_key: Optional[str] = None, *, timeout: float = 30.0):
    self.client = httpx.Client(base_url=self.base_url, timeout=timeout)

def get_current_price(self, symbol: str) -> Optional[dict]:
    response = self.client.get(url, params=params)

After:

def __init__(
    self,
    api_key: Optional[str] = None,
    *,
    client: Optional[httpx.AsyncClient] = None,
    timeout: float = 3.0
):
    self.client = client or httpx.AsyncClient(
        base_url=self.base_url,
        timeout=timeout,
        limits=httpx.Limits(max_connections=20, max_keepalive_connections=10)
    )

async def get_current_price(self, symbol: str) -> Optional[dict]:
    response = await self.client.get(url, params=params)

File: gold_sentiment.py

Before:

for category, tickers in proxy_config.items():
    for ticker in tickers:
        data = self._get_proxy_short_data(ticker)
        if data:
            proxy_data[ticker] = data

After:

import asyncio

# Collect all tasks
tasks = []
ticker_list = []
for category, tickers in proxy_config.items():
    for ticker in tickers:
        tasks.append(self._get_proxy_short_data(ticker))
        ticker_list.append((ticker, category))

# Execute in parallel
results = await asyncio.gather(*tasks, return_exceptions=True)

# Process results
for (ticker, category), data in zip(ticker_list, results):
    if data and not isinstance(data, Exception):
        proxy_data[ticker] = {**data, 'category': category}

File: api.py

Add at startup:

import httpx
from cachetools import TTLCache

# Shared HTTP client
_http_client = httpx.AsyncClient(
    timeout=3.0,
    limits=httpx.Limits(
        max_connections=20,
        max_keepalive_connections=10,
        keepalive_expiry=30.0
    )
)

# Simple caches
_price_cache = TTLCache(maxsize=100, ttl=5.0)
_sentiment_cache = TTLCache(maxsize=10, ttl=300.0)
_indicator_cache = TTLCache(maxsize=20, ttl=900.0)

@app.on_event("startup")
async def startup():
    logger.info("Starting up with connection pooling enabled")

@app.on_event("shutdown")
async def shutdown():
    await _http_client.aclose()

17. Risk Assessment

Low Risk Changes

  • ✅ Reduce timeouts (easy rollback)
  • ✅ Add in-memory caching (feature flag)
  • ✅ Connection pooling (standard practice)
  • ✅ Mobile request deduplication (isolated)

Medium Risk Changes

  • ⚠️ Async conversion (requires thorough testing)
  • ⚠️ Parallel gold sentiment (error handling complexity)
  • ⚠️ Shared HTTP client (thread safety concerns)

High Risk Changes

  • 🔴 Redis integration (new infrastructure dependency)
  • 🔴 HTTP/2 (potential NGINX compatibility issues)
  • 🔴 Optimistic UI (can confuse users if not well-designed)

18. Success Metrics

Before Optimization (Baseline)

Metric Value
/rate-signal (XAUUSD) p95 7-9 seconds
/rate-signal (others) p95 1-1.5 seconds
/interpret p95 500-800ms
Cache hit rate 0%
Polygon API calls/day 700 (per 100 requests)
User complaints "Too slow"

After Phase 1+2 (Target)

Metric Value Improvement
/rate-signal (XAUUSD) p95 1-1.5 seconds 82% faster
/rate-signal (others) p95 400-700ms 50% faster
/interpret p95 250-400ms 40% faster
Cache hit rate 50-70% New metric
Polygon API calls/day 250 (per 100 requests) 64% reduction
User feedback "Much faster!" Subjective improvement

Conclusion

The TradeCalc application has significant performance optimization opportunities, particularly in the /rate-signal endpoint for gold (XAUUSD) which currently takes 5-8 seconds due to sequential API calls.

Critical Path Forward:

  1. Immediate (Phase 1): Apply quick wins for 30-40% improvement
  2. Next Sprint (Phase 2): Async conversion for 70-80% improvement on gold
  3. Following Sprint (Phase 3): Full caching layer for 50-90% cache hit improvement

Investment vs Return:

  • Phase 1+2: ~8 hours of work → 5-7x faster gold signals
  • Phase 3: +3 hours → 50-90% fewer API calls
  • Phase 4: +4 hours → 40-60% better perceived performance

The most impactful change is parallelizing gold sentiment analysis (Phase 2), which alone reduces the critical path from 5-8 seconds to 800-1200ms.

Files Referenced:

  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/api.py
  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/polygon.py
  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/polygon_price.py
  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/gold_sentiment.py
  • /home/claude-dev/repos/tradecalc/src/tradecalc/services/indicators.py
  • /home/claude-dev/repos/tradecalc/mobile/src/services/api.ts
  • /home/claude-dev/repos/tradecalc/mobile/src/screens/SignalRatingScreen.tsx
  • /home/claude-dev/repos/tradecalc/ecosystem.mobile_calc.config.js