Stock Price History

curl "https://stocks-dev.up.railway.app/api/v1/stock/AAPL/history?outputsize=compact"

import requests
import pandas as pd
import matplotlib.pyplot as plt

response = requests.get("https://stocks-dev.up.railway.app/api/v1/stock/AAPL/history")
data = response.json()

symbol = data['symbol']
historical = data['historical']

print(f"📈 Historical Data for {symbol} (Last {len(historical)} days)")
print("=" * 60)

# Convert to DataFrame for easy analysis
df = pd.DataFrame(historical)
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values('date')  # Sort chronologically

# Recent performance
recent = historical[:5]  # Last 5 days
print("Recent Performance:")
for day in recent:
    date = day['date']
    close = day['close']
    change_pct = day['changePercent']
    volume = day['volume']
    
    emoji = "🟢" if change_pct >= 0 else "🔴"
    vol_str = f"{volume/1e6:.1f}M" if volume >= 1e6 else f"{volume/1e3:.0f}K"
    
    print(f"  {date}: ${close:>7.2f} {emoji} {change_pct:>+5.2f}% | Vol: {vol_str}")

# Calculate key statistics
df['returns'] = df['adjClose'].pct_change()
volatility = df['returns'].std() * (252 ** 0.5) * 100  # Annualized volatility
avg_volume = df['volume'].mean()
max_price = df['high'].max()
min_price = df['low'].min()

print(f"\n📊 Statistics (Last {len(historical)} days):")
print(f"  Price Range: ${min_price:.2f} - ${max_price:.2f}")
print(f"  Avg Volume: {avg_volume/1e6:.1f}M shares")
print(f"  Volatility: {volatility:.1f}% (annualized)")

# Simple moving averages
if len(df) >= 20:
    df['sma_20'] = df['adjClose'].rolling(20).mean()
    current_price = df['adjClose'].iloc[-1]
    sma_20 = df['sma_20'].iloc[-1]
    
    print(f"  20-day SMA: ${sma_20:.2f}")
    print(f"  vs SMA: {((current_price - sma_20) / sma_20 * 100):+.1f}%")

const response = await fetch('https://stocks-dev.up.railway.app/api/v1/stock/AAPL/history');
const data = await response.json();

const symbol = data.symbol;
const historical = data.historical;

console.log(`📈 Historical Data for ${symbol} (Last ${historical.length} days)`);
console.log("=".repeat(60));

// Recent performance
const recent = historical.slice(0, 5); // Last 5 days
console.log("Recent Performance:");
recent.forEach(day => {
    const date = day.date;
    const close = day.close;
    const changePct = day.changePercent;
    const volume = day.volume;
    
    const emoji = changePct >= 0 ? "🟢" : "🔴";
    const volStr = volume >= 1e6 ? `${(volume/1e6).toFixed(1)}M` : `${(volume/1e3).toFixed(0)}K`;
    
    console.log(`  ${date}: $${close.toFixed(2).padStart(7)} ${emoji} ${changePct >= 0 ? '+' : ''}${changePct.toFixed(2)}% | Vol: ${volStr}`);
});

// Calculate key statistics
const prices = historical.map(d => d.adjClose);
const volumes = historical.map(d => d.volume);
const maxPrice = Math.max(...historical.map(d => d.high));
const minPrice = Math.min(...historical.map(d => d.low));
const avgVolume = volumes.reduce((a, b) => a + b, 0) / volumes.length;

console.log(`\n📊 Statistics (Last ${historical.length} days):`);
console.log(`  Price Range: $${minPrice.toFixed(2)} - $${maxPrice.toFixed(2)}`);
console.log(`  Avg Volume: ${(avgVolume/1e6).toFixed(1)}M shares`);

// Simple moving average
if (historical.length >= 20) {
    const last20 = prices.slice(0, 20);
    const sma20 = last20.reduce((a, b) => a + b, 0) / last20.length;
    const currentPrice = prices[0];
    
    console.log(`  20-day SMA: $${sma20.toFixed(2)}`);
    console.log(`  vs SMA: ${((currentPrice - sma20) / sma20 * 100) >= 0 ? '+' : ''}${((currentPrice - sma20) / sma20 * 100).toFixed(1)}%`);
}

<?php
$response = file_get_contents('https://stocks-dev.up.railway.app/api/v1/stock/AAPL/history');
$data = json_decode($response, true);

$symbol = $data['symbol'];
$historical = $data['historical'];

echo "📈 Historical Data for $symbol (Last " . count($historical) . " days)\n";
echo str_repeat("=", 60) . "\n";

// Recent performance
$recent = array_slice($historical, 0, 5); // Last 5 days
echo "Recent Performance:\n";
foreach ($recent as $day) {
    $date = $day['date'];
    $close = $day['close'];
    $change_pct = $day['changePercent'];
    $volume = $day['volume'];
    
    $emoji = $change_pct >= 0 ? "🟢" : "🔴";
    $vol_str = $volume >= 1e6 ? number_format($volume/1e6, 1) . "M" : number_format($volume/1e3, 0) . "K";
    
    echo sprintf("  %s: $%7.2f %s %+5.2f%% | Vol: %s\n", 
        $date, $close, $emoji, $change_pct, $vol_str);
}

// Calculate key statistics
$prices = array_column($historical, 'adjClose');
$volumes = array_column($historical, 'volume');
$highs = array_column($historical, 'high');
$lows = array_column($historical, 'low');

$max_price = max($highs);
$min_price = min($lows);
$avg_volume = array_sum($volumes) / count($volumes);

echo "\n📊 Statistics (Last " . count($historical) . " days):\n";
echo "  Price Range: $" . number_format($min_price, 2) . " - $" . number_format($max_price, 2) . "\n";
echo "  Avg Volume: " . number_format($avg_volume/1e6, 1) . "M shares\n";

// Simple moving average
if (count($historical) >= 20) {
    $last_20 = array_slice($prices, 0, 20);
    $sma_20 = array_sum($last_20) / count($last_20);
    $current_price = $prices[0];
    
    echo "  20-day SMA: $" . number_format($sma_20, 2) . "\n";
    echo "  vs SMA: " . sprintf("%+.1f", (($current_price - $sma_20) / $sma_20 * 100)) . "%\n";
}
?>