Update app.py
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app.py
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import gradio as gr
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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from datetime import datetime
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import yfinance as yf
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# βββββββββββββββββββββββββββββββββββββββββββββ
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#
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# βββββββββββββββββββββββββββββββββββββββββββββ
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apply_theme(
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|
| 930 |
demo.launch()
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import numpy as np
|
| 4 |
+
import plotly.graph_objects as go
|
| 5 |
+
from datetime import datetime
|
| 6 |
+
import yfinance as yf
|
| 7 |
+
|
| 8 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 9 |
+
# Color Palette
|
| 10 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 11 |
+
BG_CARD = "#ffffff"
|
| 12 |
+
BORDER = "#e2e8f0"
|
| 13 |
+
BLUE_PRIMARY = "#2563eb"
|
| 14 |
+
BLUE_DARK = "#1e3a8a"
|
| 15 |
+
BLUE_LIGHT = "#eff6ff"
|
| 16 |
+
GREEN = "#059669"
|
| 17 |
+
RED = "#dc2626"
|
| 18 |
+
GOLD = "#d97706"
|
| 19 |
+
TEXT_DARK = "#0f172a"
|
| 20 |
+
TEXT_MED = "#475569"
|
| 21 |
+
TEXT_LIGHT = "#94a3b8"
|
| 22 |
+
|
| 23 |
+
PLOTLY_THEME = dict(
|
| 24 |
+
paper_bgcolor="#ffffff",
|
| 25 |
+
plot_bgcolor="#f8faff",
|
| 26 |
+
font=dict(family="DM Sans, sans-serif", color=TEXT_DARK, size=12),
|
| 27 |
+
legend=dict(bgcolor="rgba(255,255,255,0.95)", bordercolor=BORDER,
|
| 28 |
+
borderwidth=1, font=dict(color=TEXT_DARK)),
|
| 29 |
+
margin=dict(l=55, r=30, t=55, b=45),
|
| 30 |
+
)
|
| 31 |
+
AXIS_STYLE = dict(
|
| 32 |
+
gridcolor="#e8f0fb", zerolinecolor="#cbd5e1",
|
| 33 |
+
tickfont=dict(color=TEXT_LIGHT), linecolor=BORDER,
|
| 34 |
+
)
|
| 35 |
+
|
| 36 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 37 |
+
# CSS
|
| 38 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 39 |
+
CUSTOM_CSS = """
|
| 40 |
+
@import url('https://fonts.googleapis.com/css2?family=DM+Sans:wght@300;400;500;600;700&family=Syne:wght@600;700;800&family=DM+Mono:wght@400;500&display=swap');
|
| 41 |
+
|
| 42 |
+
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
|
| 43 |
+
|
| 44 |
+
body, .gradio-container, .gradio-container * {
|
| 45 |
+
font-family: 'DM Sans', sans-serif !important;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
/* ββ Page background ββ */
|
| 49 |
+
.gradio-container {
|
| 50 |
+
background: #f1f5f9 !important;
|
| 51 |
+
max-width: 100% !important;
|
| 52 |
+
padding: 0 !important;
|
| 53 |
+
}
|
| 54 |
+
|
| 55 |
+
/* ββ Hero header ββ */
|
| 56 |
+
.pf-hero {
|
| 57 |
+
background: linear-gradient(135deg, #0f172a 0%, #1e3a8a 50%, #1d4ed8 100%);
|
| 58 |
+
padding: 40px 48px 32px;
|
| 59 |
+
position: relative;
|
| 60 |
+
overflow: hidden;
|
| 61 |
+
border-bottom: 3px solid #f59e0b;
|
| 62 |
+
}
|
| 63 |
+
.pf-hero::before {
|
| 64 |
+
content: '';
|
| 65 |
+
position: absolute;
|
| 66 |
+
top: -60px; right: -60px;
|
| 67 |
+
width: 320px; height: 320px;
|
| 68 |
+
background: radial-gradient(circle, rgba(99,102,241,0.15) 0%, transparent 70%);
|
| 69 |
+
border-radius: 50%;
|
| 70 |
+
}
|
| 71 |
+
.pf-hero::after {
|
| 72 |
+
content: '';
|
| 73 |
+
position: absolute;
|
| 74 |
+
bottom: -40px; left: 10%;
|
| 75 |
+
width: 200px; height: 200px;
|
| 76 |
+
background: radial-gradient(circle, rgba(251,191,36,0.08) 0%, transparent 70%);
|
| 77 |
+
border-radius: 50%;
|
| 78 |
+
}
|
| 79 |
+
.pf-logo {
|
| 80 |
+
display: inline-flex;
|
| 81 |
+
align-items: center;
|
| 82 |
+
gap: 12px;
|
| 83 |
+
margin-bottom: 14px;
|
| 84 |
+
}
|
| 85 |
+
.pf-logo-hex {
|
| 86 |
+
width: 44px; height: 44px;
|
| 87 |
+
background: linear-gradient(135deg, #f59e0b, #fbbf24);
|
| 88 |
+
clip-path: polygon(50% 0%, 100% 25%, 100% 75%, 50% 100%, 0% 75%, 0% 25%);
|
| 89 |
+
display: flex; align-items: center; justify-content: center;
|
| 90 |
+
font-size: 20px; font-weight: 800; color: #0f172a;
|
| 91 |
+
}
|
| 92 |
+
.pf-title {
|
| 93 |
+
font-family: 'Syne', sans-serif !important;
|
| 94 |
+
font-size: 1.9rem;
|
| 95 |
+
font-weight: 800;
|
| 96 |
+
color: #ffffff !important;
|
| 97 |
+
letter-spacing: -0.5px;
|
| 98 |
+
line-height: 1.1;
|
| 99 |
+
}
|
| 100 |
+
.pf-title span { color: #fbbf24; }
|
| 101 |
+
.pf-sub {
|
| 102 |
+
font-size: 0.78rem;
|
| 103 |
+
color: rgba(255,255,255,0.55) !important;
|
| 104 |
+
letter-spacing: 2.5px;
|
| 105 |
+
text-transform: uppercase;
|
| 106 |
+
margin-top: 10px;
|
| 107 |
+
}
|
| 108 |
+
.pf-pills {
|
| 109 |
+
display: flex;
|
| 110 |
+
gap: 8px;
|
| 111 |
+
margin-top: 16px;
|
| 112 |
+
flex-wrap: wrap;
|
| 113 |
+
}
|
| 114 |
+
.pf-pill {
|
| 115 |
+
padding: 4px 12px;
|
| 116 |
+
border-radius: 20px;
|
| 117 |
+
font-size: 0.68rem;
|
| 118 |
+
font-weight: 600;
|
| 119 |
+
letter-spacing: 0.8px;
|
| 120 |
+
text-transform: uppercase;
|
| 121 |
+
border: 1px solid rgba(255,255,255,0.15);
|
| 122 |
+
color: rgba(255,255,255,0.75) !important;
|
| 123 |
+
background: rgba(255,255,255,0.07);
|
| 124 |
+
}
|
| 125 |
+
|
| 126 |
+
/* ββ Control bar ββ */
|
| 127 |
+
.pf-controls {
|
| 128 |
+
background: #ffffff;
|
| 129 |
+
border-bottom: 1px solid #e2e8f0;
|
| 130 |
+
padding: 14px 24px;
|
| 131 |
+
box-shadow: 0 2px 8px rgba(15,23,42,0.06);
|
| 132 |
+
}
|
| 133 |
+
|
| 134 |
+
/* ββ Tabs ββ */
|
| 135 |
+
div[role="tablist"] {
|
| 136 |
+
background: #ffffff !important;
|
| 137 |
+
border-bottom: 2px solid #e2e8f0 !important;
|
| 138 |
+
padding: 0 20px !important;
|
| 139 |
+
gap: 0 !important;
|
| 140 |
+
}
|
| 141 |
+
div[role="tab"] {
|
| 142 |
+
font-family: 'DM Sans', sans-serif !important;
|
| 143 |
+
font-size: 0.81rem !important;
|
| 144 |
+
font-weight: 500 !important;
|
| 145 |
+
color: #64748b !important;
|
| 146 |
+
border: none !important;
|
| 147 |
+
border-bottom: 3px solid transparent !important;
|
| 148 |
+
padding: 14px 18px !important;
|
| 149 |
+
background: transparent !important;
|
| 150 |
+
border-radius: 0 !important;
|
| 151 |
+
transition: all 0.2s ease !important;
|
| 152 |
+
white-space: nowrap !important;
|
| 153 |
+
}
|
| 154 |
+
div[role="tab"]:hover {
|
| 155 |
+
color: #2563eb !important;
|
| 156 |
+
background: #eff6ff !important;
|
| 157 |
+
}
|
| 158 |
+
div[role="tab"][aria-selected="true"] {
|
| 159 |
+
color: #2563eb !important;
|
| 160 |
+
border-bottom: 3px solid #2563eb !important;
|
| 161 |
+
font-weight: 700 !important;
|
| 162 |
+
background: #eff6ff !important;
|
| 163 |
+
}
|
| 164 |
+
|
| 165 |
+
/* ββ Tab content wrapper ββ */
|
| 166 |
+
.tab-content-wrap {
|
| 167 |
+
padding: 24px;
|
| 168 |
+
background: #f1f5f9;
|
| 169 |
+
min-height: 400px;
|
| 170 |
+
}
|
| 171 |
+
|
| 172 |
+
/* ββ KPI cards ββ */
|
| 173 |
+
.kpi-row {
|
| 174 |
+
display: grid;
|
| 175 |
+
gap: 14px;
|
| 176 |
+
margin-bottom: 20px;
|
| 177 |
+
}
|
| 178 |
+
.kpi-row-5 { grid-template-columns: repeat(5, 1fr); }
|
| 179 |
+
.kpi-row-4 { grid-template-columns: repeat(4, 1fr); }
|
| 180 |
+
.kpi-row-3 { grid-template-columns: repeat(3, 1fr); }
|
| 181 |
+
|
| 182 |
+
.kpi {
|
| 183 |
+
background: #ffffff;
|
| 184 |
+
border: 1px solid #e2e8f0;
|
| 185 |
+
border-radius: 14px;
|
| 186 |
+
padding: 18px 20px 16px;
|
| 187 |
+
position: relative;
|
| 188 |
+
overflow: hidden;
|
| 189 |
+
box-shadow: 0 1px 6px rgba(15,23,42,0.05);
|
| 190 |
+
transition: box-shadow 0.2s, transform 0.2s;
|
| 191 |
+
}
|
| 192 |
+
.kpi:hover {
|
| 193 |
+
box-shadow: 0 6px 20px rgba(15,23,42,0.10);
|
| 194 |
+
transform: translateY(-2px);
|
| 195 |
+
}
|
| 196 |
+
.kpi-accent {
|
| 197 |
+
position: absolute;
|
| 198 |
+
top: 0; left: 0; right: 0;
|
| 199 |
+
height: 3px;
|
| 200 |
+
background: linear-gradient(90deg, #2563eb, #60a5fa);
|
| 201 |
+
border-radius: 14px 14px 0 0;
|
| 202 |
+
}
|
| 203 |
+
.kpi-accent.g { background: linear-gradient(90deg, #059669, #34d399); }
|
| 204 |
+
.kpi-accent.r { background: linear-gradient(90deg, #dc2626, #f87171); }
|
| 205 |
+
.kpi-accent.o { background: linear-gradient(90deg, #d97706, #fbbf24); }
|
| 206 |
+
|
| 207 |
+
.kpi-label {
|
| 208 |
+
font-size: 0.68rem;
|
| 209 |
+
font-weight: 600;
|
| 210 |
+
color: #94a3b8;
|
| 211 |
+
text-transform: uppercase;
|
| 212 |
+
letter-spacing: 1.2px;
|
| 213 |
+
margin-bottom: 8px;
|
| 214 |
+
}
|
| 215 |
+
.kpi-val {
|
| 216 |
+
font-family: 'Syne', sans-serif !important;
|
| 217 |
+
font-size: 1.55rem;
|
| 218 |
+
font-weight: 700;
|
| 219 |
+
color: #0f172a;
|
| 220 |
+
line-height: 1;
|
| 221 |
+
}
|
| 222 |
+
.kpi-val.g { color: #059669; }
|
| 223 |
+
.kpi-val.r { color: #dc2626; }
|
| 224 |
+
.kpi-val.o { color: #d97706; }
|
| 225 |
+
.kpi-sub {
|
| 226 |
+
font-size: 0.71rem;
|
| 227 |
+
color: #94a3b8;
|
| 228 |
+
margin-top: 5px;
|
| 229 |
+
font-weight: 500;
|
| 230 |
+
}
|
| 231 |
+
.kpi-sub.g { color: #059669; }
|
| 232 |
+
.kpi-sub.r { color: #dc2626; }
|
| 233 |
+
|
| 234 |
+
/* ββ Section header ββ */
|
| 235 |
+
.sec-hdr {
|
| 236 |
+
display: flex;
|
| 237 |
+
align-items: center;
|
| 238 |
+
gap: 10px;
|
| 239 |
+
margin-bottom: 16px;
|
| 240 |
+
padding-bottom: 12px;
|
| 241 |
+
border-bottom: 1px solid #e2e8f0;
|
| 242 |
+
}
|
| 243 |
+
.sec-hdr-icon {
|
| 244 |
+
width: 34px; height: 34px;
|
| 245 |
+
background: #eff6ff;
|
| 246 |
+
border-radius: 8px;
|
| 247 |
+
display: flex; align-items: center; justify-content: center;
|
| 248 |
+
font-size: 16px;
|
| 249 |
+
}
|
| 250 |
+
.sec-hdr-text { flex: 1; }
|
| 251 |
+
.sec-hdr-title {
|
| 252 |
+
font-family: 'Syne', sans-serif !important;
|
| 253 |
+
font-size: 0.95rem;
|
| 254 |
+
font-weight: 700;
|
| 255 |
+
color: #0f172a;
|
| 256 |
+
}
|
| 257 |
+
.sec-hdr-sub {
|
| 258 |
+
font-size: 0.72rem;
|
| 259 |
+
color: #94a3b8;
|
| 260 |
+
margin-top: 2px;
|
| 261 |
+
}
|
| 262 |
+
|
| 263 |
+
/* ββ Banners ββ */
|
| 264 |
+
.bn {
|
| 265 |
+
border-radius: 10px;
|
| 266 |
+
padding: 11px 16px;
|
| 267 |
+
font-size: 0.81rem;
|
| 268 |
+
font-weight: 500;
|
| 269 |
+
margin-bottom: 14px;
|
| 270 |
+
display: flex;
|
| 271 |
+
align-items: center;
|
| 272 |
+
gap: 8px;
|
| 273 |
+
}
|
| 274 |
+
.bn-ok { background: #ecfdf5; border: 1px solid #6ee7b7; color: #065f46; border-left: 4px solid #059669; }
|
| 275 |
+
.bn-warn { background: #fffbeb; border: 1px solid #fcd34d; color: #78350f; border-left: 4px solid #d97706; }
|
| 276 |
+
.bn-err { background: #fef2f2; border: 1px solid #fca5a5; color: #7f1d1d; border-left: 4px solid #dc2626; }
|
| 277 |
+
.bn-info { background: #eff6ff; border: 1px solid #bfdbfe; color: #1e3a8a; border-left: 4px solid #2563eb; }
|
| 278 |
+
|
| 279 |
+
/* ββ Badge ββ */
|
| 280 |
+
.bdg {
|
| 281 |
+
display: inline-flex; align-items: center; gap: 5px;
|
| 282 |
+
padding: 5px 14px;
|
| 283 |
+
border-radius: 20px;
|
| 284 |
+
font-size: 0.72rem; font-weight: 700;
|
| 285 |
+
letter-spacing: 0.6px; text-transform: uppercase;
|
| 286 |
+
margin-bottom: 14px;
|
| 287 |
+
}
|
| 288 |
+
.bdg-ok { background: #ecfdf5; color: #065f46; border: 1px solid #6ee7b7; }
|
| 289 |
+
.bdg-warn { background: #fef2f2; color: #991b1b; border: 1px solid #fca5a5; }
|
| 290 |
+
|
| 291 |
+
/* ββ Chart wrapper ββ */
|
| 292 |
+
.chart-card {
|
| 293 |
+
background: #ffffff;
|
| 294 |
+
border: 1px solid #e2e8f0;
|
| 295 |
+
border-radius: 14px;
|
| 296 |
+
overflow: hidden;
|
| 297 |
+
box-shadow: 0 1px 6px rgba(15,23,42,0.04);
|
| 298 |
+
margin-bottom: 16px;
|
| 299 |
+
}
|
| 300 |
+
|
| 301 |
+
/* ββ Buttons ββ */
|
| 302 |
+
button.primary, .gr-button-primary {
|
| 303 |
+
font-family: 'DM Sans', sans-serif !important;
|
| 304 |
+
font-weight: 700 !important;
|
| 305 |
+
font-size: 0.83rem !important;
|
| 306 |
+
letter-spacing: 0.3px !important;
|
| 307 |
+
background: linear-gradient(135deg, #1d4ed8, #2563eb) !important;
|
| 308 |
+
color: #ffffff !important;
|
| 309 |
+
border: none !important;
|
| 310 |
+
border-radius: 10px !important;
|
| 311 |
+
box-shadow: 0 4px 14px rgba(37,99,235,0.30) !important;
|
| 312 |
+
transition: all 0.2s !important;
|
| 313 |
+
padding: 10px 24px !important;
|
| 314 |
+
}
|
| 315 |
+
button.primary:hover {
|
| 316 |
+
background: linear-gradient(135deg, #1e40af, #1d4ed8) !important;
|
| 317 |
+
box-shadow: 0 6px 20px rgba(37,99,235,0.40) !important;
|
| 318 |
+
transform: translateY(-1px) !important;
|
| 319 |
+
}
|
| 320 |
+
|
| 321 |
+
/* ββ Inputs / dropdowns ββ */
|
| 322 |
+
input, select, textarea, .gr-dropdown {
|
| 323 |
+
font-family: 'DM Sans', sans-serif !important;
|
| 324 |
+
background: #ffffff !important;
|
| 325 |
+
border: 1.5px solid #e2e8f0 !important;
|
| 326 |
+
border-radius: 10px !important;
|
| 327 |
+
color: #0f172a !important;
|
| 328 |
+
font-size: 0.88rem !important;
|
| 329 |
+
transition: border-color 0.2s, box-shadow 0.2s !important;
|
| 330 |
+
}
|
| 331 |
+
input:focus, select:focus {
|
| 332 |
+
border-color: #2563eb !important;
|
| 333 |
+
box-shadow: 0 0 0 3px rgba(37,99,235,0.10) !important;
|
| 334 |
+
outline: none !important;
|
| 335 |
+
}
|
| 336 |
+
label {
|
| 337 |
+
font-family: 'DM Sans', sans-serif !important;
|
| 338 |
+
font-size: 0.73rem !important;
|
| 339 |
+
font-weight: 600 !important;
|
| 340 |
+
color: #475569 !important;
|
| 341 |
+
letter-spacing: 0.6px !important;
|
| 342 |
+
text-transform: uppercase !important;
|
| 343 |
+
}
|
| 344 |
+
|
| 345 |
+
/* ββ Tables ββ */
|
| 346 |
+
table {
|
| 347 |
+
border-collapse: separate !important;
|
| 348 |
+
border-spacing: 0 !important;
|
| 349 |
+
width: 100% !important;
|
| 350 |
+
background: #ffffff !important;
|
| 351 |
+
border-radius: 12px !important;
|
| 352 |
+
overflow: hidden !important;
|
| 353 |
+
border: 1px solid #e2e8f0 !important;
|
| 354 |
+
font-size: 0.82rem !important;
|
| 355 |
+
box-shadow: 0 1px 6px rgba(15,23,42,0.04) !important;
|
| 356 |
+
}
|
| 357 |
+
th {
|
| 358 |
+
background: #1e3a8a !important;
|
| 359 |
+
color: #ffffff !important;
|
| 360 |
+
font-size: 0.70rem !important;
|
| 361 |
+
font-weight: 700 !important;
|
| 362 |
+
letter-spacing: 1px !important;
|
| 363 |
+
text-transform: uppercase !important;
|
| 364 |
+
padding: 12px 16px !important;
|
| 365 |
+
white-space: nowrap !important;
|
| 366 |
+
}
|
| 367 |
+
td {
|
| 368 |
+
color: #0f172a !important;
|
| 369 |
+
padding: 10px 16px !important;
|
| 370 |
+
border-bottom: 1px solid #f1f5f9 !important;
|
| 371 |
+
background: #ffffff !important;
|
| 372 |
+
font-family: 'DM Mono', monospace !important;
|
| 373 |
+
font-size: 0.80rem !important;
|
| 374 |
+
}
|
| 375 |
+
tr:nth-child(even) td { background: #f8fafc !important; }
|
| 376 |
+
tr:hover td { background: #eff6ff !important; }
|
| 377 |
+
|
| 378 |
+
/* ββ Slider ββ */
|
| 379 |
+
input[type="range"] { accent-color: #2563eb !important; }
|
| 380 |
+
|
| 381 |
+
/* ββ Footer ββ */
|
| 382 |
+
.pf-footer {
|
| 383 |
+
background: #0f172a;
|
| 384 |
+
color: rgba(255,255,255,0.4);
|
| 385 |
+
text-align: center;
|
| 386 |
+
padding: 20px;
|
| 387 |
+
font-size: 0.70rem;
|
| 388 |
+
letter-spacing: 1.8px;
|
| 389 |
+
text-transform: uppercase;
|
| 390 |
+
margin-top: 32px;
|
| 391 |
+
border-top: 2px solid #1e3a8a;
|
| 392 |
+
}
|
| 393 |
+
|
| 394 |
+
/* ββ Scrollbar ββ */
|
| 395 |
+
::-webkit-scrollbar { width: 5px; height: 5px; }
|
| 396 |
+
::-webkit-scrollbar-track { background: #f1f5f9; }
|
| 397 |
+
::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 3px; }
|
| 398 |
+
::-webkit-scrollbar-thumb:hover { background: #2563eb; }
|
| 399 |
+
|
| 400 |
+
/* ββ Accordion ββ */
|
| 401 |
+
.gr-accordion {
|
| 402 |
+
background: #ffffff !important;
|
| 403 |
+
border: 1px solid #e2e8f0 !important;
|
| 404 |
+
border-radius: 12px !important;
|
| 405 |
+
overflow: hidden !important;
|
| 406 |
+
}
|
| 407 |
+
|
| 408 |
+
/* ββ Responsive ββ */
|
| 409 |
+
@media (max-width: 768px) {
|
| 410 |
+
.kpi-row-5 { grid-template-columns: repeat(2, 1fr) !important; }
|
| 411 |
+
.kpi-row-4 { grid-template-columns: repeat(2, 1fr) !important; }
|
| 412 |
+
.pf-title { font-size: 1.4rem !important; }
|
| 413 |
+
.pf-hero { padding: 24px 20px !important; }
|
| 414 |
+
}
|
| 415 |
+
"""
|
| 416 |
+
|
| 417 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 418 |
+
# HTML helpers
|
| 419 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 420 |
+
def kpi(label, value, sub="", accent="b"):
|
| 421 |
+
ac = {"g": "g", "r": "r", "o": "o"}.get(accent, "")
|
| 422 |
+
vc = ac
|
| 423 |
+
sc = "g" if ("β²" in sub or "+" in sub) else "r" if ("βΌ" in sub or (sub.startswith("-") and sub != "-")) else ""
|
| 424 |
+
s_html = f'<div class="kpi-sub {sc}">{sub}</div>' if sub else ""
|
| 425 |
+
return f"""<div class="kpi">
|
| 426 |
+
<div class="kpi-accent {ac}"></div>
|
| 427 |
+
<div class="kpi-label">{label}</div>
|
| 428 |
+
<div class="kpi-val {vc}">{value}</div>
|
| 429 |
+
{s_html}
|
| 430 |
+
</div>"""
|
| 431 |
+
|
| 432 |
+
def sec(icon, title, sub=""):
|
| 433 |
+
s = f'<div class="sec-hdr-sub">{sub}</div>' if sub else ""
|
| 434 |
+
return f"""<div class="sec-hdr">
|
| 435 |
+
<div class="sec-hdr-icon">{icon}</div>
|
| 436 |
+
<div class="sec-hdr-text">
|
| 437 |
+
<div class="sec-hdr-title">{title}</div>
|
| 438 |
+
{s}
|
| 439 |
+
</div>
|
| 440 |
+
</div>"""
|
| 441 |
+
|
| 442 |
+
def banner(msg, kind="info"):
|
| 443 |
+
icons = {"ok": "β
", "warn": "β οΈ", "err": "π«", "info": "βΉοΈ"}
|
| 444 |
+
css = {"ok": "bn-ok", "warn": "bn-warn", "err": "bn-err", "info": "bn-info"}
|
| 445 |
+
return f'<div class="bn {css.get(kind,"bn-info")}">{icons.get(kind,"βΉοΈ")} {msg}</div>'
|
| 446 |
+
|
| 447 |
+
SYMBOLS = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN', 'RELIANCE.NS']
|
| 448 |
+
|
| 449 |
+
# βββββββββββββββββββββοΏ½οΏ½οΏ½βββββββββββββββββββββββ
|
| 450 |
+
# Data helpers
|
| 451 |
+
# βββββββββββββββββββββββββββββββββββββββββββββ
|
| 452 |
+
def get_realtime(symbols):
|
| 453 |
+
results = {}
|
| 454 |
+
for sym in symbols:
|
| 455 |
+
try:
|
| 456 |
+
info = yf.Ticker(sym).info
|
| 457 |
+
price = info.get('currentPrice') or info.get('regularMarketPrice') or 0
|
| 458 |
+
open_ = info.get('regularMarketOpen') or price
|
| 459 |
+
results[sym] = {
|
| 460 |
+
'symbol': sym, 'price': price, 'open': open_,
|
| 461 |
+
'high': info.get('dayHigh', 0), 'low': info.get('dayLow', 0),
|
| 462 |
+
'volume': info.get('volume', 0), 'market_cap': info.get('marketCap', 0),
|
| 463 |
+
'pe_ratio': info.get('trailingPE', 0),
|
| 464 |
+
'change_pct': ((price - open_) / open_ * 100) if open_ else 0,
|
| 465 |
+
}
|
| 466 |
+
except Exception:
|
| 467 |
+
results[sym] = {k: 0 for k in ['price','open','high','low','volume','market_cap','pe_ratio','change_pct']}
|
| 468 |
+
results[sym]['symbol'] = sym
|
| 469 |
+
return results
|
| 470 |
+
|
| 471 |
+
def get_historical(symbol, period="1y"):
|
| 472 |
+
try:
|
| 473 |
+
df = yf.Ticker(symbol).history(period=period)
|
| 474 |
+
return df if not df.empty else pd.DataFrame()
|
| 475 |
+
except Exception:
|
| 476 |
+
return pd.DataFrame()
|
| 477 |
+
|
| 478 |
+
def get_returns(symbol, period="1y"):
|
| 479 |
+
df = get_historical(symbol, period)
|
| 480 |
+
if df.empty:
|
| 481 |
+
return pd.Series(dtype=float)
|
| 482 |
+
return df['Close'].pct_change().dropna()
|
| 483 |
+
|
| 484 |
+
def compute_risk(returns, portfolio_value, rf=0.04):
|
| 485 |
+
if returns.empty:
|
| 486 |
+
return {}
|
| 487 |
+
daily_vol = returns.std()
|
| 488 |
+
annual_vol = daily_vol * np.sqrt(252)
|
| 489 |
+
total_ret = (1 + returns).prod() - 1
|
| 490 |
+
years = max(len(returns) / 252, 0.01)
|
| 491 |
+
ann_ret = (1 + total_ret) ** (1 / years) - 1
|
| 492 |
+
sharpe = (ann_ret - rf) / annual_vol if annual_vol else 0
|
| 493 |
+
var_95 = abs(np.percentile(returns, 5))
|
| 494 |
+
var_99 = abs(np.percentile(returns, 1))
|
| 495 |
+
tail = returns[returns <= -var_95]
|
| 496 |
+
cvar_95 = abs(tail.mean()) if len(tail) else var_95
|
| 497 |
+
cum = (1 + returns).cumprod()
|
| 498 |
+
peak = cum.cummax()
|
| 499 |
+
dd = (cum - peak) / peak
|
| 500 |
+
max_dd = abs(dd.min())
|
| 501 |
+
return dict(
|
| 502 |
+
annual_vol=annual_vol, daily_vol=daily_vol, ann_ret=ann_ret, sharpe=sharpe,
|
| 503 |
+
var_95=var_95, var_99=var_99, cvar_95=cvar_95,
|
| 504 |
+
var_95_usd=var_95*portfolio_value, var_99_usd=var_99*portfolio_value,
|
| 505 |
+
cvar_95_usd=cvar_95*portfolio_value, max_dd=max_dd, drawdown=dd, returns=returns,
|
| 506 |
+
)
|
| 507 |
+
|
| 508 |
+
def sharpe_label(s):
|
| 509 |
+
if s > 3: return "Exceptional", "g"
|
| 510 |
+
if s > 2: return "Very Good", "g"
|
| 511 |
+
if s > 1: return "Good", "b"
|
| 512 |
+
if s > 0.5: return "Acceptable", "o"
|
| 513 |
+
if s > 0: return "Poor", "o"
|
| 514 |
+
return "Losing Money", "r"
|
| 515 |
+
|
| 516 |
+
def apply_theme(fig, title_text=None, yaxis_title=None, xaxis_title=None, extra=None):
|
| 517 |
+
layout = dict(**PLOTLY_THEME)
|
| 518 |
+
layout['xaxis'] = dict(**AXIS_STYLE)
|
| 519 |
+
layout['yaxis'] = dict(**AXIS_STYLE)
|
| 520 |
+
if title_text:
|
| 521 |
+
layout['title'] = dict(text=title_text, font=dict(color="#0f172a", size=13, family="Syne, sans-serif"))
|
| 522 |
+
if yaxis_title: layout['yaxis']['title'] = dict(text=yaxis_title, font=dict(color=TEXT_MED))
|
| 523 |
+
if xaxis_title: layout['xaxis']['title'] = dict(text=xaxis_title, font=dict(color=TEXT_MED))
|
| 524 |
+
if extra: layout.update(extra)
|
| 525 |
+
fig.update_layout(**layout)
|
| 526 |
+
return fig
|
| 527 |
+
|
| 528 |
+
# βββββββββββββββββββββββββββββββββββββββββββββββ
|
| 529 |
+
# TAB RENDER FUNCTIONS
|
| 530 |
+
# βββββββββββββββββββββββββββββββββββββββββββββββ
|
| 531 |
+
|
| 532 |
+
def render_market_overview():
|
| 533 |
+
data = get_realtime(SYMBOLS)
|
| 534 |
+
ts = datetime.now().strftime('%d %b %Y %H:%M:%S')
|
| 535 |
+
|
| 536 |
+
# KPI cards
|
| 537 |
+
cards = '<div class="kpi-row kpi-row-5" style="margin-bottom:20px">'
|
| 538 |
+
for sym, d in data.items():
|
| 539 |
+
chg = d.get('change_pct', 0)
|
| 540 |
+
sign = "β²" if chg >= 0 else "βΌ"
|
| 541 |
+
acc = "g" if chg >= 0 else "r"
|
| 542 |
+
display_sym = sym.replace('.NS', '')
|
| 543 |
+
cards += kpi(display_sym, f"${d['price']:.2f}" if d['price'] else "β",
|
| 544 |
+
f"{sign} {abs(chg):.2f}%", acc)
|
| 545 |
+
cards += "</div>"
|
| 546 |
+
|
| 547 |
+
prices = {s.replace('.NS',''): d['price'] for s, d in data.items() if d['price']}
|
| 548 |
+
changes = {s.replace('.NS',''): d['change_pct'] for s, d in data.items()}
|
| 549 |
+
bcolors = [GREEN if changes.get(s,0) >= 0 else RED for s in prices]
|
| 550 |
+
|
| 551 |
+
fig_p = go.Figure()
|
| 552 |
+
fig_p.add_trace(go.Bar(
|
| 553 |
+
x=list(prices.keys()), y=list(prices.values()),
|
| 554 |
+
marker=dict(color=bcolors, line=dict(color='white', width=1.5),
|
| 555 |
+
opacity=0.9),
|
| 556 |
+
text=[f"${v:.2f}" for v in prices.values()],
|
| 557 |
+
textposition='outside', textfont=dict(size=11, color=TEXT_DARK, family="DM Mono"),
|
| 558 |
+
hovertemplate="<b>%{x}</b><br>Price: $%{y:.2f}<extra></extra>",
|
| 559 |
+
))
|
| 560 |
+
apply_theme(fig_p, title_text="Current Stock Prices (USD)", yaxis_title="Price ($)",
|
| 561 |
+
extra={"showlegend": False, "bargap": 0.3})
|
| 562 |
+
|
| 563 |
+
vols = {s.replace('.NS',''): d.get('volume', 0) for s, d in data.items()}
|
| 564 |
+
fig_v = go.Figure()
|
| 565 |
+
fig_v.add_trace(go.Bar(
|
| 566 |
+
x=list(vols.keys()), y=list(vols.values()),
|
| 567 |
+
marker=dict(color=list(vols.values()),
|
| 568 |
+
colorscale=[[0,"#bfdbfe"],[1,"#1d4ed8"]],
|
| 569 |
+
showscale=False, line=dict(color='white', width=1.5), opacity=0.9),
|
| 570 |
+
text=[f"{v/1e6:.1f}M" for v in vols.values()],
|
| 571 |
+
textposition='outside', textfont=dict(size=11, color=TEXT_DARK, family="DM Mono"),
|
| 572 |
+
hovertemplate="<b>%{x}</b><br>Volume: %{y:,.0f}<extra></extra>",
|
| 573 |
+
))
|
| 574 |
+
apply_theme(fig_v, title_text="Trading Volume", yaxis_title="Volume",
|
| 575 |
+
extra={"showlegend": False, "bargap": 0.3})
|
| 576 |
+
|
| 577 |
+
rows = []
|
| 578 |
+
for s, d in data.items():
|
| 579 |
+
chg = d.get('change_pct', 0)
|
| 580 |
+
rows.append({
|
| 581 |
+
'Symbol': s.replace('.NS',''),
|
| 582 |
+
'Price ($)': f"${d['price']:.2f}" if d['price'] else "β",
|
| 583 |
+
'Open ($)': f"${d['open']:.2f}" if d['open'] else "β",
|
| 584 |
+
'High ($)': f"${d['high']:.2f}" if d['high'] else "β",
|
| 585 |
+
'Low ($)': f"${d['low']:.2f}" if d['low'] else "β",
|
| 586 |
+
'Volume': f"{d['volume']/1e6:.1f}M" if d['volume'] else "β",
|
| 587 |
+
'Mkt Cap': f"${d['market_cap']/1e12:.2f}T" if d.get('market_cap') else "β",
|
| 588 |
+
'P/E': f"{d['pe_ratio']:.1f}" if d.get('pe_ratio') else "β",
|
| 589 |
+
'Change': f"{'β²' if chg >= 0 else 'βΌ'} {abs(chg):.2f}%",
|
| 590 |
+
})
|
| 591 |
+
|
| 592 |
+
return (
|
| 593 |
+
cards, fig_p, fig_v, pd.DataFrame(rows),
|
| 594 |
+
banner(f"Data refreshed at {ts}", "ok")
|
| 595 |
+
)
|
| 596 |
+
|
| 597 |
+
|
| 598 |
+
def render_historical(symbol, period):
|
| 599 |
+
df = get_historical(symbol, period)
|
| 600 |
+
if df.empty:
|
| 601 |
+
return None, None, None, banner("No data available for this symbol/period.", "err")
|
| 602 |
+
|
| 603 |
+
fig_c = go.Figure()
|
| 604 |
+
fig_c.add_trace(go.Candlestick(
|
| 605 |
+
x=df.index, open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'],
|
| 606 |
+
increasing=dict(line=dict(color=GREEN, width=1.2), fillcolor="rgba(5,150,105,0.20)"),
|
| 607 |
+
decreasing=dict(line=dict(color=RED, width=1.2), fillcolor="rgba(220,38,38,0.20)"),
|
| 608 |
+
name="OHLC",
|
| 609 |
+
))
|
| 610 |
+
ma20 = df['Close'].rolling(20).mean()
|
| 611 |
+
fig_c.add_trace(go.Scatter(
|
| 612 |
+
x=df.index, y=ma20, name="MA 20",
|
| 613 |
+
line=dict(color=BLUE_PRIMARY, width=1.8, dash='dot'),
|
| 614 |
+
))
|
| 615 |
+
apply_theme(fig_c, title_text=f"{symbol} β Price Chart with MA20 ({period})",
|
| 616 |
+
yaxis_title="Price (USD)", extra={"xaxis_rangeslider_visible": False})
|
| 617 |
+
|
| 618 |
+
returns = df['Close'].pct_change().dropna()
|
| 619 |
+
cum_ret = (1 + returns).cumprod() - 1
|
| 620 |
+
col_ret = GREEN if cum_ret.iloc[-1] >= 0 else RED
|
| 621 |
+
fig_r = go.Figure()
|
| 622 |
+
fig_r.add_trace(go.Scatter(
|
| 623 |
+
x=cum_ret.index, y=cum_ret * 100, fill='tozeroy',
|
| 624 |
+
fillcolor="rgba(5,150,105,0.08)" if cum_ret.iloc[-1] >= 0 else "rgba(220,38,38,0.08)",
|
| 625 |
+
line=dict(color=col_ret, width=2.2), name="Cumulative Return",
|
| 626 |
+
hovertemplate="%{x|%b %d, %Y}<br>Return: %{y:.2f}%<extra></extra>",
|
| 627 |
+
))
|
| 628 |
+
fig_r.add_hline(y=0, line=dict(color=TEXT_LIGHT, dash='dash', width=1))
|
| 629 |
+
apply_theme(fig_r, title_text="Cumulative Return (%)", yaxis_title="Return (%)")
|
| 630 |
+
|
| 631 |
+
vcols = [GREEN if c >= o else RED for c, o in zip(df['Close'], df['Open'])]
|
| 632 |
+
fig_v = go.Figure()
|
| 633 |
+
fig_v.add_trace(go.Bar(
|
| 634 |
+
x=df.index, y=df['Volume'], marker_color=vcols, name="Volume",
|
| 635 |
+
opacity=0.8,
|
| 636 |
+
hovertemplate="%{x|%b %d}<br>Vol: %{y:,.0f}<extra></extra>",
|
| 637 |
+
))
|
| 638 |
+
apply_theme(fig_v, title_text="Volume (Green = Up Day Β· Red = Down Day)", yaxis_title="Volume")
|
| 639 |
+
|
| 640 |
+
total = cum_ret.iloc[-1] * 100
|
| 641 |
+
sign = "β²" if total >= 0 else "βΌ"
|
| 642 |
+
acc = "g" if total >= 0 else "r"
|
| 643 |
+
stats = f"""
|
| 644 |
+
{sec("π", f"Historical Analysis β {symbol}", f"Period: {period} Β· {len(df)} trading days")}
|
| 645 |
+
<div class="kpi-row kpi-row-4">
|
| 646 |
+
{kpi("Current Price", f"${df['Close'].iloc[-1]:.2f}", "", "b")}
|
| 647 |
+
{kpi("Period High", f"${df['High'].max():.2f}", "", "g")}
|
| 648 |
+
{kpi("Period Low", f"${df['Low'].min():.2f}", "", "r")}
|
| 649 |
+
{kpi("Total Return", f"{sign} {abs(total):.2f}%", "", acc)}
|
| 650 |
+
</div>"""
|
| 651 |
+
|
| 652 |
+
return fig_c, fig_r, fig_v, stats
|
| 653 |
+
|
| 654 |
+
|
| 655 |
+
def render_risk(symbol, portfolio_value):
|
| 656 |
+
returns = get_returns(symbol, "1y")
|
| 657 |
+
if returns.empty:
|
| 658 |
+
return banner("Could not fetch data for this symbol.", "err"), None, None, None, None
|
| 659 |
+
|
| 660 |
+
m = compute_risk(returns, portfolio_value)
|
| 661 |
+
slabel, scol = sharpe_label(m['sharpe'])
|
| 662 |
+
risk_ok = m['annual_vol'] < 0.30 and m['max_dd'] < 0.20 and m['sharpe'] > 1.0
|
| 663 |
+
badge = f'<span class="bdg {"bdg-ok" if risk_ok else "bdg-warn"}">{"β Within Limits" if risk_ok else "β Risk Alert"}</span>'
|
| 664 |
+
|
| 665 |
+
kpi_html = f"""
|
| 666 |
+
{sec("π‘οΈ", f"Risk Assessment β {symbol}", f"Portfolio: ${portfolio_value:,.0f} Β· {len(returns)} trading days")}
|
| 667 |
+
{badge}
|
| 668 |
+
<div class="kpi-row kpi-row-4" style="margin-top:12px">
|
| 669 |
+
{kpi("VaR 95%", f"{m['var_95']:.2%}", f"β${m['var_95_usd']:,.0f} / day", "r")}
|
| 670 |
+
{kpi("VaR 99%", f"{m['var_99']:.2%}", f"β${m['var_99_usd']:,.0f} / day", "r")}
|
| 671 |
+
{kpi("CVaR 95%", f"{m['cvar_95']:.2%}", "Expected Shortfall", "r")}
|
| 672 |
+
{kpi("Annual Vol", f"{m['annual_vol']:.2%}", f"Daily: {m['daily_vol']:.2%}",
|
| 673 |
+
"o" if m['annual_vol'] > 0.25 else "b")}
|
| 674 |
+
</div>
|
| 675 |
+
<div class="kpi-row kpi-row-4">
|
| 676 |
+
{kpi("Max Drawdown", f"{m['max_dd']:.2%}", "Peak-to-Trough",
|
| 677 |
+
"r" if m['max_dd'] > 0.20 else "o")}
|
| 678 |
+
{kpi("Sharpe Ratio", f"{m['sharpe']:.2f}", slabel, scol)}
|
| 679 |
+
{kpi("Annual Return", f"{m['ann_ret']:.2%}",
|
| 680 |
+
"β² Positive" if m['ann_ret'] >= 0 else "βΌ Negative",
|
| 681 |
+
"g" if m['ann_ret'] >= 0 else "r")}
|
| 682 |
+
{kpi("Data Points", str(len(returns)), "Trading Days", "b")}
|
| 683 |
+
</div>"""
|
| 684 |
+
|
| 685 |
+
# Distribution
|
| 686 |
+
fig_dist = go.Figure()
|
| 687 |
+
fig_dist.add_trace(go.Histogram(
|
| 688 |
+
x=returns * 100, nbinsx=55,
|
| 689 |
+
marker=dict(color=BLUE_PRIMARY, opacity=0.75, line=dict(color='white', width=0.5)),
|
| 690 |
+
name="Daily Returns",
|
| 691 |
+
hovertemplate="Return: %{x:.2f}%<br>Count: %{y}<extra></extra>",
|
| 692 |
+
))
|
| 693 |
+
fig_dist.add_vline(x=-m['var_95']*100, line=dict(color=GOLD, dash='dash', width=2),
|
| 694 |
+
annotation=dict(text="VaR 95%", font=dict(color=GOLD, size=10)))
|
| 695 |
+
fig_dist.add_vline(x=-m['var_99']*100, line=dict(color=RED, dash='dash', width=2),
|
| 696 |
+
annotation=dict(text="VaR 99%", font=dict(color=RED, size=10)))
|
| 697 |
+
apply_theme(fig_dist, title_text="Return Distribution + VaR Lines",
|
| 698 |
+
xaxis_title="Daily Return (%)", yaxis_title="Frequency")
|
| 699 |
+
|
| 700 |
+
# Drawdown
|
| 701 |
+
dd = m['drawdown']
|
| 702 |
+
fig_dd = go.Figure()
|
| 703 |
+
fig_dd.add_trace(go.Scatter(
|
| 704 |
+
x=dd.index, y=dd * 100, fill='tozeroy',
|
| 705 |
+
fillcolor="rgba(220,38,38,0.10)",
|
| 706 |
+
line=dict(color=RED, width=1.8), name="Drawdown %",
|
| 707 |
+
hovertemplate="%{x|%b %d, %Y}<br>Drawdown: %{y:.2f}%<extra></extra>",
|
| 708 |
+
))
|
| 709 |
+
fig_dd.add_hline(y=-m['max_dd']*100, line=dict(color=GOLD, dash='dot', width=1.5),
|
| 710 |
+
annotation=dict(text=f"Max DD {m['max_dd']:.2%}", font=dict(color=GOLD, size=10)))
|
| 711 |
+
apply_theme(fig_dd, title_text="Underwater Drawdown Chart", yaxis_title="Drawdown (%)")
|
| 712 |
+
|
| 713 |
+
# Rolling vol
|
| 714 |
+
rv = returns.rolling(21).std() * np.sqrt(252) * 100
|
| 715 |
+
fig_rv = go.Figure()
|
| 716 |
+
fig_rv.add_trace(go.Scatter(
|
| 717 |
+
x=rv.index, y=rv, fill='tozeroy', fillcolor="rgba(37,99,235,0.08)",
|
| 718 |
+
line=dict(color=BLUE_PRIMARY, width=2), name="21-day Vol",
|
| 719 |
+
hovertemplate="%{x|%b %d, %Y}<br>Vol: %{y:.2f}%<extra></extra>",
|
| 720 |
+
))
|
| 721 |
+
fig_rv.add_hline(y=30, line=dict(color=RED, dash='dash', width=1.3),
|
| 722 |
+
annotation=dict(text="Risk Limit 30%", font=dict(color=RED, size=10)))
|
| 723 |
+
apply_theme(fig_rv, title_text="Rolling 21-Day Annualised Volatility", yaxis_title="Volatility (%)")
|
| 724 |
+
|
| 725 |
+
# Gauge
|
| 726 |
+
risk_score = min(100, m['annual_vol']/0.5*40 + m['max_dd']/0.5*40 + max(0,1-m['sharpe'])*20)
|
| 727 |
+
gcol = GREEN if risk_score < 40 else GOLD if risk_score < 70 else RED
|
| 728 |
+
fig_g = go.Figure(go.Indicator(
|
| 729 |
+
mode="gauge+number",
|
| 730 |
+
value=risk_score,
|
| 731 |
+
title=dict(text="COMPOSITE RISK SCORE", font=dict(family="DM Sans", size=11, color=TEXT_MED)),
|
| 732 |
+
number=dict(font=dict(family="Syne", size=36, color=gcol)),
|
| 733 |
+
gauge=dict(
|
| 734 |
+
axis=dict(range=[0,100], tickwidth=1, tickcolor=TEXT_LIGHT,
|
| 735 |
+
tickfont=dict(family="DM Sans", size=10, color=TEXT_LIGHT)),
|
| 736 |
+
bar=dict(color=gcol, thickness=0.22),
|
| 737 |
+
bgcolor="#ffffff", borderwidth=1, bordercolor=BORDER,
|
| 738 |
+
steps=[dict(range=[0,40], color="rgba(5,150,105,0.07)"),
|
| 739 |
+
dict(range=[40,70], color="rgba(217,119,6,0.07)"),
|
| 740 |
+
dict(range=[70,100],color="rgba(220,38,38,0.07)")],
|
| 741 |
+
threshold=dict(line=dict(color=gcol, width=3), thickness=0.75, value=risk_score),
|
| 742 |
+
),
|
| 743 |
+
))
|
| 744 |
+
fig_g.update_layout(paper_bgcolor="#ffffff", font=dict(family="DM Sans", color=TEXT_DARK),
|
| 745 |
+
height=260, margin=dict(l=30,r=30,t=60,b=20))
|
| 746 |
+
|
| 747 |
+
return kpi_html, fig_dist, fig_dd, fig_rv, fig_g
|
| 748 |
+
|
| 749 |
+
|
| 750 |
+
SCENARIOS = {
|
| 751 |
+
"Moderate β5%": -0.05,
|
| 752 |
+
"Correction β10%": -0.10,
|
| 753 |
+
"Bear Market β20%": -0.20,
|
| 754 |
+
"Severe β30%": -0.30,
|
| 755 |
+
"2008 Crisis β50%": -0.50,
|
| 756 |
+
"COVID β35%": -0.35,
|
| 757 |
+
"Flash Crash β10%": -0.10,
|
| 758 |
+
"Rate Shock β15%": -0.15,
|
| 759 |
+
}
|
| 760 |
+
|
| 761 |
+
def render_stress(symbol, portfolio_value):
|
| 762 |
+
returns = get_returns(symbol, "1y")
|
| 763 |
+
avg_daily = returns.mean() if not returns.empty else 0.0003
|
| 764 |
+
|
| 765 |
+
rows, pcts, dloss, labels = [], [], [], []
|
| 766 |
+
for name, shock in SCENARIOS.items():
|
| 767 |
+
shocked = portfolio_value * (1 + shock)
|
| 768 |
+
loss = portfolio_value - shocked
|
| 769 |
+
days_rec = abs(shock) / avg_daily if avg_daily > 0 else float('inf')
|
| 770 |
+
yrs_rec = round(days_rec/252, 1) if days_rec != float('inf') else None
|
| 771 |
+
rows.append({'Scenario': name, 'Market Shock': f"{shock:.0%}",
|
| 772 |
+
'Portfolio After': f"${shocked:,.0f}",
|
| 773 |
+
'Loss Amount': f"${loss:,.0f}",
|
| 774 |
+
'Recovery (yrs)': str(yrs_rec) if yrs_rec else "N/A"})
|
| 775 |
+
pcts.append(shock * 100)
|
| 776 |
+
dloss.append(loss)
|
| 777 |
+
labels.append(name)
|
| 778 |
+
|
| 779 |
+
def sev(l):
|
| 780 |
+
if l < -30: return RED
|
| 781 |
+
if l < -15: return GOLD
|
| 782 |
+
return BLUE_PRIMARY
|
| 783 |
+
|
| 784 |
+
fig_pct = go.Figure()
|
| 785 |
+
fig_pct.add_trace(go.Bar(
|
| 786 |
+
x=labels, y=pcts,
|
| 787 |
+
marker=dict(color=[sev(l) for l in pcts], line=dict(color='white', width=1),
|
| 788 |
+
opacity=0.85),
|
| 789 |
+
text=[f"{l:.0f}%" for l in pcts], textposition='outside',
|
| 790 |
+
textfont=dict(size=10, color=TEXT_DARK, family="DM Mono"),
|
| 791 |
+
hovertemplate="<b>%{x}</b><br>Loss: %{y:.1f}%<extra></extra>",
|
| 792 |
+
))
|
| 793 |
+
apply_theme(fig_pct, title_text="Portfolio Loss % by Scenario", yaxis_title="Loss (%)",
|
| 794 |
+
extra={"yaxis": dict(**AXIS_STYLE, range=[min(pcts)*1.3, 5]), "bargap": 0.3})
|
| 795 |
+
|
| 796 |
+
fig_usd = go.Figure()
|
| 797 |
+
fig_usd.add_trace(go.Bar(
|
| 798 |
+
x=labels, y=dloss,
|
| 799 |
+
marker=dict(color=dloss, colorscale=[[0,"#bfdbfe"],[0.5,GOLD],[1,RED]],
|
| 800 |
+
showscale=False, line=dict(color='white', width=1), opacity=0.85),
|
| 801 |
+
text=[f"${l:,.0f}" for l in dloss], textposition='outside',
|
| 802 |
+
textfont=dict(size=10, color=TEXT_DARK, family="DM Mono"),
|
| 803 |
+
hovertemplate="<b>%{x}</b><br>Loss: $%{y:,.0f}<extra></extra>",
|
| 804 |
+
))
|
| 805 |
+
apply_theme(fig_usd, title_text="Dollar Loss by Scenario", yaxis_title="Loss ($)",
|
| 806 |
+
extra={"bargap": 0.3})
|
| 807 |
+
|
| 808 |
+
return fig_pct, fig_usd, pd.DataFrame(rows)
|
| 809 |
+
|
| 810 |
+
|
| 811 |
+
def render_correlation(symbols_str):
|
| 812 |
+
syms = [s.strip().upper() for s in symbols_str.split(',') if s.strip()]
|
| 813 |
+
if len(syms) < 2:
|
| 814 |
+
return None, banner("Enter at least 2 comma-separated symbols.", "warn"), None
|
| 815 |
+
|
| 816 |
+
all_ret = {}
|
| 817 |
+
for s in syms:
|
| 818 |
+
r = get_returns(s, "1y")
|
| 819 |
+
if not r.empty:
|
| 820 |
+
all_ret[s] = r
|
| 821 |
+
|
| 822 |
+
if len(all_ret) < 2:
|
| 823 |
+
return None, banner("Could not fetch data for enough symbols.", "err"), None
|
| 824 |
+
|
| 825 |
+
df_ret = pd.DataFrame(all_ret).dropna()
|
| 826 |
+
corr = df_ret.corr()
|
| 827 |
+
|
| 828 |
+
fig_h = go.Figure(go.Heatmap(
|
| 829 |
+
z=corr.values, x=corr.columns.tolist(), y=corr.index.tolist(),
|
| 830 |
+
colorscale=[[0, RED],[0.5,"#f8fafc"],[1, BLUE_PRIMARY]],
|
| 831 |
+
zmid=0, zmin=-1, zmax=1,
|
| 832 |
+
text=corr.values.round(2), texttemplate="%{text}",
|
| 833 |
+
textfont=dict(family="DM Mono", size=12, color=TEXT_DARK),
|
| 834 |
+
hovertemplate="<b>%{x} vs %{y}</b><br>r = %{z:.3f}<extra></extra>",
|
| 835 |
+
colorbar=dict(tickfont=dict(family="DM Sans", color=TEXT_MED),
|
| 836 |
+
title=dict(text="r", font=dict(color=TEXT_MED))),
|
| 837 |
+
))
|
| 838 |
+
apply_theme(fig_h, title_text="Correlation Matrix β 1Y Daily Returns")
|
| 839 |
+
|
| 840 |
+
cum_df = (1 + df_ret).cumprod() - 1
|
| 841 |
+
palette = [BLUE_PRIMARY, GREEN, GOLD, RED, "#7c3aed", "#db2777", "#0891b2"]
|
| 842 |
+
fig_cr = go.Figure()
|
| 843 |
+
for i, col in enumerate(cum_df.columns):
|
| 844 |
+
fig_cr.add_trace(go.Scatter(
|
| 845 |
+
x=cum_df.index, y=cum_df[col]*100, name=col,
|
| 846 |
+
line=dict(color=palette[i % len(palette)], width=2.2),
|
| 847 |
+
hovertemplate=f"<b>{col}</b><br>%{{x|%b %d}}<br>Return: %{{y:.2f}}%<extra></extra>",
|
| 848 |
+
))
|
| 849 |
+
apply_theme(fig_cr, title_text="Cumulative Returns Comparison (%)", yaxis_title="Return (%)")
|
| 850 |
+
|
| 851 |
+
avg_corr = corr.values[np.triu_indices_from(corr.values, k=1)].mean()
|
| 852 |
+
if avg_corr < 0.5:
|
| 853 |
+
msg, kind = f"Well Diversified β avg pairwise correlation: {avg_corr:.3f}", "ok"
|
| 854 |
+
elif avg_corr < 0.7:
|
| 855 |
+
msg, kind = f"Moderately Correlated β avg correlation: {avg_corr:.3f}", "warn"
|
| 856 |
+
else:
|
| 857 |
+
msg, kind = f"Highly Correlated β low diversification benefit (r = {avg_corr:.3f})", "err"
|
| 858 |
+
|
| 859 |
+
return fig_h, banner(msg, kind), fig_cr
|
| 860 |
+
|
| 861 |
+
|
| 862 |
+
def render_monte_carlo(symbol, portfolio_value, days, sims):
|
| 863 |
+
days, sims = int(days), int(sims)
|
| 864 |
+
returns = get_returns(symbol, "1y")
|
| 865 |
+
if returns.empty:
|
| 866 |
+
return None, banner("Could not fetch data.", "err")
|
| 867 |
+
|
| 868 |
+
mu, sigma = returns.mean(), returns.std()
|
| 869 |
+
np.random.seed(42)
|
| 870 |
+
sim_rets = np.random.normal(mu, sigma, (days, sims))
|
| 871 |
+
sim_paths = portfolio_value * np.exp(np.cumsum(np.log(1 + sim_rets), axis=0))
|
| 872 |
+
final_vals = sim_paths[-1]
|
| 873 |
+
|
| 874 |
+
fig = go.Figure()
|
| 875 |
+
x_ax = list(range(days))
|
| 876 |
+
for i in range(min(200, sims)):
|
| 877 |
+
col = "rgba(5,150,105,0.10)" if sim_paths[-1,i] >= portfolio_value else "rgba(220,38,38,0.08)"
|
| 878 |
+
fig.add_trace(go.Scatter(x=x_ax, y=sim_paths[:,i], mode='lines',
|
| 879 |
+
line=dict(color=col, width=0.5),
|
| 880 |
+
showlegend=False, hoverinfo='skip'))
|
| 881 |
+
|
| 882 |
+
med_path = np.median(sim_paths, axis=1)
|
| 883 |
+
fig.add_trace(go.Scatter(x=x_ax, y=med_path, mode='lines',
|
| 884 |
+
line=dict(color=BLUE_PRIMARY, width=2.8), name="Median Path"))
|
| 885 |
+
|
| 886 |
+
p5 = np.percentile(sim_paths, 5, axis=1)
|
| 887 |
+
p95 = np.percentile(sim_paths, 95, axis=1)
|
| 888 |
+
fig.add_trace(go.Scatter(
|
| 889 |
+
x=x_ax + x_ax[::-1], y=list(p95)+list(p5[::-1]),
|
| 890 |
+
fill='toself', fillcolor="rgba(37,99,235,0.06)",
|
| 891 |
+
line=dict(color='rgba(0,0,0,0)'), name="90% Confidence Band",
|
| 892 |
+
))
|
| 893 |
+
fig.add_hline(y=portfolio_value, line=dict(color=TEXT_LIGHT, dash='dash', width=1.5),
|
| 894 |
+
annotation=dict(text="Initial Value", font=dict(color=TEXT_LIGHT, size=10)))
|
| 895 |
+
apply_theme(fig, title_text=f"Monte Carlo Simulation β {sims:,} Paths Β· {days} Trading Days",
|
| 896 |
+
yaxis_title="Portfolio Value ($)", xaxis_title="Trading Day")
|
| 897 |
+
|
| 898 |
+
med_fin = np.median(final_vals)
|
| 899 |
+
p5_fin = np.percentile(final_vals, 5)
|
| 900 |
+
p95_fin = np.percentile(final_vals, 95)
|
| 901 |
+
pct_profit = (final_vals >= portfolio_value).mean() * 100
|
| 902 |
+
med_ret = (med_fin / portfolio_value - 1) * 100
|
| 903 |
+
sign = "β²" if med_ret >= 0 else "βΌ"
|
| 904 |
+
|
| 905 |
+
stats = f"""
|
| 906 |
+
{sec("π²", f"Monte Carlo Results β {symbol}", f"{sims:,} simulations Β· {days} trading days")}
|
| 907 |
+
<div class="kpi-row kpi-row-4">
|
| 908 |
+
{kpi("Median Outcome", f"${med_fin:,.0f}",
|
| 909 |
+
f"{sign} {abs(med_ret):.1f}%", "g" if med_ret >= 0 else "r")}
|
| 910 |
+
{kpi("Best Case (95th)", f"${p95_fin:,.0f}",
|
| 911 |
+
f"+{(p95_fin/portfolio_value-1)*100:.1f}%", "g")}
|
| 912 |
+
{kpi("Worst Case (5th)", f"${p5_fin:,.0f}",
|
| 913 |
+
f"{(p5_fin/portfolio_value-1)*100:.1f}%", "r")}
|
| 914 |
+
{kpi("% Profitable", f"{pct_profit:.1f}%",
|
| 915 |
+
f"of {sims:,} simulations", "g" if pct_profit >= 50 else "r")}
|
| 916 |
+
</div>"""
|
| 917 |
+
|
| 918 |
+
return fig, stats
|
| 919 |
+
|
| 920 |
+
|
| 921 |
+
# βββββββββββββββββββββββββββββββββββββββββββββββ
|
| 922 |
+
# BUILD APP
|
| 923 |
+
# βββββββββββββββββββββββββββββββββββββββββββββββ
|
| 924 |
+
|
| 925 |
+
HEADER_HTML = """
|
| 926 |
+
<div class="pf-hero">
|
| 927 |
+
<div class="pf-logo">
|
| 928 |
+
<div class="pf-logo-hex">⬑</div>
|
| 929 |
+
<div>
|
| 930 |
+
<div class="pf-title">Portfolio <span>Intelligence</span> System</div>
|
| 931 |
+
</div>
|
| 932 |
+
</div>
|
| 933 |
+
<div class="pf-sub">Multi-Agent Risk & Market Analytics Platform Β· v3.0</div>
|
| 934 |
+
<div class="pf-pills">
|
| 935 |
+
<span class="pf-pill">π‘ Real-Time Data</span>
|
| 936 |
+
<span class="pf-pill">π‘οΈ Risk Metrics</span>
|
| 937 |
+
<span class="pf-pill">π² Monte Carlo</span>
|
| 938 |
+
<span class="pf-pill">π Stress Testing</span>
|
| 939 |
+
<span class="pf-pill">π Correlation</span>
|
| 940 |
+
<span class="pf-pill">IIT Madras 2026</span>
|
| 941 |
+
</div>
|
| 942 |
+
</div>
|
| 943 |
+
"""
|
| 944 |
+
|
| 945 |
+
FOOTER_HTML = """
|
| 946 |
+
<div class="pf-footer">
|
| 947 |
+
⬑ Portfolio Intelligence System · IIT Madras 2026 ·
|
| 948 |
+
Ashwini Β· Dibyendu Sarkar Β· Jyoti Ranjan Sethi Β·
|
| 949 |
+
Data: Yahoo Finance Β· Educational Use Only
|
| 950 |
+
</div>
|
| 951 |
+
"""
|
| 952 |
+
|
| 953 |
+
with gr.Blocks(title="Portfolio Intelligence System", css=CUSTOM_CSS) as demo:
|
| 954 |
+
|
| 955 |
+
gr.HTML(HEADER_HTML)
|
| 956 |
+
|
| 957 |
+
# ββ Global controls ββ
|
| 958 |
+
with gr.Row(elem_classes=["pf-controls"]):
|
| 959 |
+
shared_symbol = gr.Dropdown(
|
| 960 |
+
choices=['AAPL','GOOGL','MSFT','TSLA','AMZN','RELIANCE.NS'],
|
| 961 |
+
value="AAPL", label="π Stock Symbol", scale=2,
|
| 962 |
+
)
|
| 963 |
+
shared_period = gr.Dropdown(
|
| 964 |
+
choices=["1mo","3mo","6mo","1y","2y","5y"],
|
| 965 |
+
value="1y", label="π
Period", scale=1,
|
| 966 |
+
)
|
| 967 |
+
shared_portfolio = gr.Number(
|
| 968 |
+
value=100_000, label="π° Portfolio Value ($)",
|
| 969 |
+
minimum=1000, scale=2,
|
| 970 |
+
)
|
| 971 |
+
|
| 972 |
+
with gr.Tabs():
|
| 973 |
+
|
| 974 |
+
# ββ Tab 1: Market Overview ββ
|
| 975 |
+
with gr.Tab("π‘ Market Overview"):
|
| 976 |
+
with gr.Row():
|
| 977 |
+
overview_btn = gr.Button("π Refresh Market Data", variant="primary", size="lg", scale=1)
|
| 978 |
+
status_out = gr.HTML()
|
| 979 |
+
cards_out = gr.HTML()
|
| 980 |
+
with gr.Row():
|
| 981 |
+
price_out = gr.Plot(label="Stock Prices", show_label=False)
|
| 982 |
+
vol_out = gr.Plot(label="Trading Volume", show_label=False)
|
| 983 |
+
with gr.Accordion("π Full Price Table", open=False):
|
| 984 |
+
table_out = gr.Dataframe(interactive=False)
|
| 985 |
+
overview_btn.click(
|
| 986 |
+
fn=render_market_overview,
|
| 987 |
+
outputs=[cards_out, price_out, vol_out, table_out, status_out],
|
| 988 |
+
)
|
| 989 |
+
|
| 990 |
+
# ββ Tab 2: Historical Analysis ββ
|
| 991 |
+
with gr.Tab("π Historical Analysis"):
|
| 992 |
+
hist_btn = gr.Button("π Load Historical Data", variant="primary", size="lg")
|
| 993 |
+
hist_stat = gr.HTML()
|
| 994 |
+
with gr.Row():
|
| 995 |
+
candle = gr.Plot(label="Candlestick", show_label=False)
|
| 996 |
+
ret_chart = gr.Plot(label="Cumulative Return", show_label=False)
|
| 997 |
+
vol_hist = gr.Plot(label="Volume", show_label=False)
|
| 998 |
+
hist_btn.click(
|
| 999 |
+
fn=render_historical,
|
| 1000 |
+
inputs=[shared_symbol, shared_period],
|
| 1001 |
+
outputs=[candle, ret_chart, vol_hist, hist_stat],
|
| 1002 |
+
)
|
| 1003 |
+
|
| 1004 |
+
# ββ Tab 3: Risk Assessment ββ
|
| 1005 |
+
with gr.Tab("π‘οΈ Risk Assessment"):
|
| 1006 |
+
risk_btn = gr.Button("π Calculate Risk Metrics", variant="primary", size="lg")
|
| 1007 |
+
risk_kpi = gr.HTML()
|
| 1008 |
+
with gr.Row():
|
| 1009 |
+
gauge_out = gr.Plot(label="Risk Gauge", show_label=False)
|
| 1010 |
+
dist_out = gr.Plot(label="Distribution", show_label=False)
|
| 1011 |
+
with gr.Row():
|
| 1012 |
+
dd_out = gr.Plot(label="Drawdown", show_label=False)
|
| 1013 |
+
rv_out = gr.Plot(label="Rolling Vol", show_label=False)
|
| 1014 |
+
risk_btn.click(
|
| 1015 |
+
fn=render_risk,
|
| 1016 |
+
inputs=[shared_symbol, shared_portfolio],
|
| 1017 |
+
outputs=[risk_kpi, dist_out, dd_out, rv_out, gauge_out],
|
| 1018 |
+
)
|
| 1019 |
+
|
| 1020 |
+
# ββ Tab 4: Stress Testing ββ
|
| 1021 |
+
with gr.Tab("π₯ Stress Testing"):
|
| 1022 |
+
stress_btn = gr.Button("π₯ Run Stress Tests", variant="primary", size="lg")
|
| 1023 |
+
with gr.Row():
|
| 1024 |
+
spct = gr.Plot(label="Loss %", show_label=False)
|
| 1025 |
+
susd = gr.Plot(label="Dollar Loss", show_label=False)
|
| 1026 |
+
with gr.Accordion("π Full Stress Test Table", open=True):
|
| 1027 |
+
stbl = gr.Dataframe(interactive=False)
|
| 1028 |
+
stress_btn.click(
|
| 1029 |
+
fn=render_stress,
|
| 1030 |
+
inputs=[shared_symbol, shared_portfolio],
|
| 1031 |
+
outputs=[spct, susd, stbl],
|
| 1032 |
+
)
|
| 1033 |
+
|
| 1034 |
+
# ββ Tab 5: Correlation ββ
|
| 1035 |
+
with gr.Tab("π Correlation"):
|
| 1036 |
+
with gr.Row():
|
| 1037 |
+
sym_in = gr.Textbox(
|
| 1038 |
+
value="AAPL,GOOGL,MSFT,TSLA,AMZN",
|
| 1039 |
+
label="Symbols (comma-separated)", scale=4,
|
| 1040 |
+
)
|
| 1041 |
+
corr_btn = gr.Button("π Compute", variant="primary", scale=1)
|
| 1042 |
+
corr_inf = gr.HTML()
|
| 1043 |
+
with gr.Row():
|
| 1044 |
+
heat_out = gr.Plot(label="Heatmap", show_label=False)
|
| 1045 |
+
cmp_out = gr.Plot(label="Returns Comparison", show_label=False)
|
| 1046 |
+
corr_btn.click(
|
| 1047 |
+
fn=render_correlation,
|
| 1048 |
+
inputs=[sym_in],
|
| 1049 |
+
outputs=[heat_out, corr_inf, cmp_out],
|
| 1050 |
+
)
|
| 1051 |
+
|
| 1052 |
+
# ββ Tab 6: Monte Carlo ββ
|
| 1053 |
+
with gr.Tab("π² Monte Carlo"):
|
| 1054 |
+
with gr.Row():
|
| 1055 |
+
mc_days = gr.Slider(21, 504, value=252, step=21, label="π
Simulation Days")
|
| 1056 |
+
mc_sims = gr.Slider(100, 1000, value=500, step=100, label="π’ Simulations")
|
| 1057 |
+
mc_btn = gr.Button("π² Run Simulation", variant="primary", size="lg")
|
| 1058 |
+
mc_stats = gr.HTML()
|
| 1059 |
+
mc_chart = gr.Plot(label="Simulation Paths", show_label=False)
|
| 1060 |
+
mc_btn.click(
|
| 1061 |
+
fn=render_monte_carlo,
|
| 1062 |
+
inputs=[shared_symbol, shared_portfolio, mc_days, mc_sims],
|
| 1063 |
+
outputs=[mc_chart, mc_stats],
|
| 1064 |
+
)
|
| 1065 |
+
|
| 1066 |
+
# ββ Tab 7: About ββ
|
| 1067 |
+
with gr.Tab("βΉοΈ About"):
|
| 1068 |
+
gr.Markdown("""
|
| 1069 |
+
## ⬑ Portfolio Intelligence System
|
| 1070 |
+
|
| 1071 |
+
A **multi-agent AI-powered platform** for comprehensive portfolio risk analysis and market intelligence. Built as part of the IIT Madras Multi-Agent Systems curriculum.
|
| 1072 |
+
|
| 1073 |
+
---
|
| 1074 |
+
|
| 1075 |
+
### 𧩠Modules
|
| 1076 |
+
|
| 1077 |
+
| Module | Description |
|
| 1078 |
+
|---|---|
|
| 1079 |
+
| **π‘ Market Overview** | Real-time prices, volume, P/E, market cap for 6 stocks |
|
| 1080 |
+
| **π Historical Analysis** | Candlestick + MA20, cumulative returns, volume |
|
| 1081 |
+
| **π‘οΈ Risk Assessment** | VaR, CVaR, Sharpe, Max Drawdown, Rolling Vol, Risk Gauge |
|
| 1082 |
+
| **π₯ Stress Testing** | 8 crash scenarios β loss % and dollar impact |
|
| 1083 |
+
| **π Correlation** | Correlation heatmap + cumulative return comparison |
|
| 1084 |
+
| **π² Monte Carlo** | Up to 1000 simulation paths with confidence bands |
|
| 1085 |
+
|
| 1086 |
+
---
|
| 1087 |
+
|
| 1088 |
+
### ποΈ Architecture
|
| 1089 |
+
|
| 1090 |
+
```
|
| 1091 |
+
Yahoo Finance API
|
| 1092 |
+
β
|
| 1093 |
+
Market Data Agent β SQLite DB
|
| 1094 |
+
β
|
| 1095 |
+
Risk Management Agent (RiskIQ)
|
| 1096 |
+
β
|
| 1097 |
+
Gradio Dashboard UI
|
| 1098 |
+
```
|
| 1099 |
+
|
| 1100 |
+
---
|
| 1101 |
+
|
| 1102 |
+
### π Risk Metrics Reference
|
| 1103 |
+
|
| 1104 |
+
| Metric | Good Value | Description |
|
| 1105 |
+
|---|---|---|
|
| 1106 |
+
| VaR 95% | < 2% | Max 1-day loss with 95% confidence |
|
| 1107 |
+
| CVaR 95% | < 3% | Avg loss when VaR is exceeded |
|
| 1108 |
+
| Sharpe Ratio | > 1.0 | Return per unit of risk |
|
| 1109 |
+
| Max Drawdown | < 20% | Worst peak-to-trough decline |
|
| 1110 |
+
| Annual Volatility | < 25% | Annualised return fluctuation |
|
| 1111 |
+
|
| 1112 |
+
---
|
| 1113 |
+
|
| 1114 |
+
**Team:** Ashwini Β· Dibyendu Sarkar Β· Jyoti Ranjan Sethi
|
| 1115 |
+
**Program:** Multi-Agent Systems Β· IIT Madras Β· Week 3 of 16 Β· 2026
|
| 1116 |
+
|
| 1117 |
+
> β οΈ *For educational purposes only. Not financial advice. Data sourced from Yahoo Finance.*
|
| 1118 |
+
""")
|
| 1119 |
+
|
| 1120 |
+
gr.HTML(FOOTER_HTML)
|
| 1121 |
+
|
| 1122 |
+
|
| 1123 |
+
if __name__ == "__main__":
|
| 1124 |
demo.launch()
|