import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch, Rectangle, Circle
from matplotlib.lines import Line2D
import numpy as np
Brand palette — gold/black financial product
plt.rcParams['font.family'] = 'DejaVu Sans'
plt.rcParams['font.size'] = 10
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def box(x, y, w, h, label, fill=GREY_LIGHT, edge=BLACK, fontsize=9, weight='normal', textcolor=BLACK):
b = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.05,rounding_size=0.1",
facecolor=fill, edgecolor=edge, linewidth=1.5)
ax.add_patch(b)
ax.text(x + w/2, y + h/2, label, ha='center', va='center',
fontsize=fontsize, fontweight=weight, color=textcolor, wrap=True)
def arrow(x1, y1, x2, y2, color=GREY):
a = FancyArrowPatch((x1, y1), (x2, y2), arrowstyle='->', mutation_scale=15,
color=color, linewidth=1.5)
ax.add_patch(a)
Title
Layer 1: External data sources
Layer 2: Fetch & validate
Layer 3: 15 agents
Layer 4: Signal engine
Layer 5: Storage & messaging
Layer 6: API + frontend
Connecting arrows
plt.savefig('/home/claude/whitepaper/01_architecture.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("01_architecture.png done")
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ax.text(6, 13.5, 'Signal Engine — End-to-End Pipeline', ha='center',
fontsize=16, fontweight='bold', color=BLACK)
steps = [
("1. FETCH", "Pull all inputs in parallel\nReject if older than staleness threshold", GOLD_LIGHT, GOLD_DARK),
("2. VALIDATE", "Gap, spike, cross-source agreement\nHealth Monitor logs failures", GREY_LIGHT, GREY),
("3. SCORE PER LAYER", "Intermarket / Macro / Positioning / Sentiment / Pattern\nEach → [-1, +1] direction + [0, 1] conviction", GOLD_LIGHT, GOLD_DARK),
("4. SESSION WEIGHT", "Shanghai × 0.4 | LME × 0.6 | COMEX × 1.0 | Globex × 0.3", BLACK, GOLD),
("5. AGGREGATE", "Weighted sum → composite ∈ [-100, +100]\nThreshold: |conviction| > 20 to emit BUY / SELL", GOLD_LIGHT, GOLD_DARK),
("6. RISK LEVELS", "ATR(14) on hourly bars\nStop = entry ± 1.5×ATR | Target = swing or 2.5×ATR | min 1:2 R:R", GREY_LIGHT, GREY),
("7. RECORD", "SQLite: timestamp, layer scores, ATR, entry / stop / target", GOLD_LIGHT, GOLD_DARK),
("8. PUBLISH", "Redis master_signals → WebSocket → REST → /agents UI", GREY_LIGHT, GREY),
("9. TRACK OUTCOME", "Learning pipeline checks +1h / +4h / +24h\nUpdates pattern win rates across 9 patterns", GOLD, BLACK),
]
y_start = 12.2
y_step = 1.25
for i, (title, body, fill, accent) in enumerate(steps):
y = y_start - i * y_step
textcolor = WHITE if fill == BLACK else BLACK
# Step number badge
circle = Circle((1, y + 0.3), 0.3, facecolor=accent, edgecolor=BLACK, linewidth=1.5, zorder=3)
ax.add_patch(circle)
ax.text(1, y + 0.3, str(i+1), ha='center', va='center', fontsize=12, fontweight='bold',
color=WHITE if accent != GOLD else BLACK, zorder=4)
# Main box
box_p = FancyBboxPatch((1.7, y - 0.1), 9.5, 0.85, boxstyle="round,pad=0.05,rounding_size=0.1",
facecolor=fill, edgecolor=accent, linewidth=2)
ax.add_patch(box_p)
ax.text(2, y + 0.5, title, fontsize=11, fontweight='bold', color=textcolor)
ax.text(2, y + 0.1, body, fontsize=9, color=textcolor)
# Connector arrow
if i < len(steps) - 1:
arrow_p = FancyArrowPatch((1, y - 0.15), (1, y - y_step + 0.65),
arrowstyle='->', mutation_scale=12, color=GOLD_DARK, linewidth=2)
ax.add_patch(arrow_p)
plt.savefig('/home/claude/whitepaper/02_signal_pipeline.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("02_signal_pipeline.png done")
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Time axis 0-24
Sessions with weights
Key event markers
Agent firing legend below
plt.savefig('/home/claude/whitepaper/03_session_timeline.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("03_session_timeline.png done")
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agents = ['Master', 'Session', 'COMEX Hourly', 'Intermarket', 'COT', 'Options', 'BLS',
'Fed/Warsh', 'Trump PhD', 'LME', 'Shanghai Prem', 'Health Mon', 'Diagnostics',
'Signal Rec', 'Social Media']
cadences = ['Real-time', '5 min', '30 min', 'Hourly', '4 hours', 'Daily', 'Weekly']
Build matrix: 1 = active at that cadence
Custom colormap: white → gold
im = ax.imshow(matrix, aspect='auto', cmap=cmap, vmin=0, vmax=1)
ax.set_xticks(range(len(cadences)))
ax.set_xticklabels(cadences, fontsize=10, fontweight='bold')
ax.set_yticks(range(len(agents)))
ax.set_yticklabels(agents, fontsize=10)
ax.set_title('Agent Cadence Matrix — 15 Agents Across 7 Cadences', fontsize=14, fontweight='bold', pad=15)
Add dots/checks
Grid
ax.set_xlim(-0.5, len(cadences)-0.5)
ax.set_ylim(len(agents)-0.5, -0.5)
plt.tight_layout()
plt.savefig('/home/claude/whitepaper/04_agent_matrix.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("04_agent_matrix.png done")
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categories = ['Charting\nDepth', 'Real-time\nData', 'Asset\nBreadth', 'Autonomous\nSynthesis',
'COMEX\nFocus', 'Multilingual\nNarrative', 'Macro\nIntegration', 'Learning /\nFeedback',
'Affordability', 'Mobile UX']
N = len(categories)
angles = [n / float(N) * 2 * np.pi for n in range(N)]
angles += angles[:1]
Scores out of 10 (honest self-assessment)
mtd_scores += mtd_scores[:1]
tv_scores += tv_scores[:1]
bb_scores += bb_scores[:1]
ax.plot(angles, mtd_scores, color=GOLD_DARK, linewidth=2.5, label='Metals Trading Desk')
ax.fill(angles, mtd_scores, color=GOLD, alpha=0.35)
ax.plot(angles, tv_scores, color=BLUE, linewidth=2, label='TradingView', linestyle='--')
ax.fill(angles, tv_scores, color=BLUE, alpha=0.1)
ax.plot(angles, bb_scores, color=GREY_DARK, linewidth=2, label='Bloomberg Terminal', linestyle=':')
ax.fill(angles, bb_scores, color=GREY, alpha=0.1)
ax.set_xticks(angles[:-1])
ax.set_xticklabels(categories, fontsize=10, fontweight='bold')
ax.set_ylim(0, 10)
ax.set_yticks([2, 4, 6, 8, 10])
ax.set_yticklabels(['2', '4', '6', '8', '10'], fontsize=8)
ax.set_title('Platform Comparison — Honest Self-Assessment', fontsize=14, fontweight='bold', pad=25)
ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1), fontsize=10)
ax.grid(color=GREY_LIGHT)
plt.tight_layout()
plt.savefig('/home/claude/whitepaper/05_radar.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("05_radar.png done")
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Left: layer contributions (current live snapshot from whitepaper)
colors = [RED if c < 0 else GREEN for c in contributions]
bars = ax1.barh(layers, contributions, color=colors, edgecolor=BLACK, linewidth=1.2)
ax1.axvline(0, color=BLACK, linewidth=1.5)
ax1.set_xlim(-1.1, 1.1)
ax1.set_xlabel('Direction × Conviction (−1 = strong sell, +1 = strong buy)', fontsize=10, fontweight='bold')
ax1.set_title('Live Layer Contributions\n(Shanghai session, weight 0.7)', fontsize=12, fontweight='bold')
for i, (bar, c) in enumerate(zip(bars, contributions)):
ax1.text(c + (0.05 if c > 0 else -0.05), i, f'{c:+.2f}',
va='center', ha='left' if c > 0 else 'right', fontsize=10, fontweight='bold')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.grid(axis='x', alpha=0.3)
Right: composite gauge
plt.tight_layout()
plt.savefig('/home/claude/whitepaper/06_conviction.png', dpi=200, bbox_inches='tight', facecolor=WHITE)
plt.close()
print("06_conviction.png done")
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Dark background with gold accent grid
Simulated candlestick chart silhouette
Title overlay
Gold accent line
plt.savefig('/home/claude/whitepaper/00_cover.png', dpi=200, bbox_inches='tight', facecolor=BLACK)
plt.close()
print("00_cover.png done")
print("\nAll images generated.")