{"spec_id":"histogram-returns-distribution","library":"plotnine","language":"python","code":"\"\"\" anyplot.ai\nhistogram-returns-distribution: Returns Distribution Histogram\nLibrary: plotnine 0.15.4 | Python 3.13.13\nQuality: 90/100 | Updated: 2026-05-20\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom plotnine import (\n    aes,\n    annotate,\n    element_blank,\n    element_line,\n    element_rect,\n    element_text,\n    geom_histogram,\n    geom_line,\n    geom_vline,\n    ggplot,\n    labs,\n    scale_fill_manual,\n    theme,\n    theme_minimal,\n)\nfrom scipy import stats\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\n\nIMPRINT = [\"#009E73\", \"#C475FD\", \"#4467A3\", \"#BD8233\", \"#AE3030\", \"#2ABCCD\", \"#954477\"]\n\n# Data - 252 trading days with slight fat tails (mixture of normals)\nnp.random.seed(42)\nn_days = 252\nnormal_returns = np.random.normal(0.0005, 0.015, int(n_days * 0.9))\nfat_tail_returns = np.random.normal(0, 0.04, int(n_days * 0.1))\nreturns = np.concatenate([normal_returns, fat_tail_returns])\nnp.random.shuffle(returns)\nreturns = returns[:n_days]\nreturns_pct = returns * 100\n\nmean_ret = np.mean(returns_pct)\nstd_ret = np.std(returns_pct)\nskewness = stats.skew(returns_pct)\nkurtosis = stats.kurtosis(returns_pct)\n\nlower_tail = mean_ret - 2 * std_ret\nupper_tail = mean_ret + 2 * std_ret\n\ndf = pd.DataFrame({\"returns\": returns_pct})\ndf[\"region\"] = pd.cut(\n    df[\"returns\"], bins=[-np.inf, lower_tail, upper_tail, np.inf], labels=[\"Left Tail\", \"Center\", \"Right Tail\"]\n)\n\n# Normal distribution overlay (count scale to match histogram)\nx_range = np.linspace(returns_pct.min() - 1, returns_pct.max() + 1, 300)\nnormal_pdf = stats.norm.pdf(x_range, mean_ret, std_ret)\nbin_width = (returns_pct.max() - returns_pct.min()) / 30\nnormal_scaled = normal_pdf * len(returns_pct) * bin_width\ndf_normal = pd.DataFrame({\"x\": x_range, \"y\": normal_scaled})\n\nstats_label = f\"Mean: {mean_ret:.2f}%\\nStd Dev: {std_ret:.2f}%\\nSkewness: {skewness:.2f}\\nExc. Kurtosis: {kurtosis:.2f}\"\n\n# Okabe-Ito fill: center green (pos 1), both tails vermillion (pos 2)\nfill_colors = {\"Left Tail\": IMPRINT[1], \"Center\": IMPRINT[0], \"Right Tail\": IMPRINT[1]}\n\n# Plot\nplot = (\n    ggplot(df, aes(x=\"returns\", fill=\"region\"))\n    + geom_histogram(bins=30, color=PAGE_BG, alpha=0.85, size=0.3)\n    + geom_line(data=df_normal, mapping=aes(x=\"x\", y=\"y\"), color=IMPRINT[2], size=1.2, inherit_aes=False)\n    + geom_vline(xintercept=mean_ret, linetype=\"dashed\", color=INK, size=0.8)\n    + geom_vline(xintercept=lower_tail, linetype=\"dotted\", color=IMPRINT[1], size=0.8)\n    + geom_vline(xintercept=upper_tail, linetype=\"dotted\", color=IMPRINT[1], size=0.8)\n    + scale_fill_manual(values=fill_colors, name=\"Region\")\n    + annotate(\n        \"label\",\n        x=returns_pct.max() - 0.5,\n        y=max(normal_scaled) * 0.92,\n        label=stats_label,\n        ha=\"right\",\n        va=\"top\",\n        size=9,\n        fill=ELEVATED_BG,\n        color=INK,\n        label_size=0.3,\n        label_padding=0.4,\n    )\n    + labs(\n        x=\"Daily Returns (%)\", y=\"Frequency\", title=\"histogram-returns-distribution · python · plotnine · anyplot.ai\"\n    )\n    + theme_minimal()\n    + theme(\n        figure_size=(8, 4.5),\n        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),\n        panel_background=element_rect(fill=PAGE_BG),\n        panel_border=element_blank(),\n        axis_line=element_line(color=INK_SOFT),\n        panel_grid_major=element_line(color=INK_SOFT, size=0.3, alpha=0.10),\n        panel_grid_minor=element_blank(),\n        plot_title=element_text(color=INK, size=12),\n        axis_title=element_text(color=INK, size=10),\n        axis_text=element_text(color=INK_SOFT, size=8),\n        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),\n        legend_text=element_text(color=INK_SOFT, size=8),\n        legend_title=element_text(color=INK, size=8),\n        legend_position=\"right\",\n    )\n)\n\n# Save\nplot.save(f\"plot-{THEME}.png\", dpi=400, width=8, height=4.5, units=\"in\", verbose=False)\n"}