docs: fix 404 broken links

- Replace non-existent Python examples with actual files
- Fix all placeholder yourusername URLs to ThotDjehuty
- Remove references to non-existent optimal_control.md theory doc
- Update examples to reference: hmm_regime_detection.py, parallel_de_benchmark.py, polaroid_optimizr_integration.py, timeseries_integration.py

examples/notebooks/01_hmm_tutorial.ipynb

@@ -2,10 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c263c5be",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ OptimizR HMM Module Loaded Successfully!\n",
+      "  Using Rust-accelerated Baum-Welch and Viterbi algorithms\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -14,7 +23,8 @@
     "# Set random seed for reproducibility\n",
     "np.random.seed(42)\n",
     "\n",
-    "print(\"OptimizR HMM Module Loaded Successfully!\")"
+    "print(\"✓ OptimizR HMM Module Loaded Successfully!\")\n",
+    "print(\"  Using Rust-accelerated Baum-Welch and Viterbi algorithms\")"
    ]
   },
   {
@@ -32,10 +42,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "f6141fe5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated 500 return observations\n",
+      "True means: [ 0.08 -0.06  0.01]\n",
+      "True stds: [0.02 0.05 0.03]\n",
+      "State distribution: [199 185 116]\n"
+     ]
+    }
+   ],
    "source": [
     "def generate_regime_data(n_samples=500, seed=42):\n",
     "    \"\"\"\n",
@@ -136,21 +157,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a2944703",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting HMM with Baum-Welch algorithm (Rust implementation)...\n",
+      "\n",
+      "✓ Training complete!\n",
+      "\n",
+      "Learned Parameters:\n",
+      "Transition Matrix:\n",
+      "  State 0: ['0.650', '0.244', '0.107']\n",
+      "  State 1: ['0.288', '0.501', '0.211']\n",
+      "  State 2: ['0.115', '0.155', '0.730']\n",
+      "\n",
+      "Emission Means: ['-0.0698', '0.0173', '0.0824']\n",
+      "Emission Stds: ['0.0446', '0.0302', '0.0183']\n"
+     ]
+    }
+   ],
    "source": [
-    "# Create and fit HMM\n",
-    "hmm = HMM(n_states=3, random_state=42)\n",
+    "# Fit HMM using Rust-accelerated implementation\n",
+    "hmm = HMM(n_states=3)\n",
     "\n",
-    "print(\"Fitting HMM with Baum-Welch algorithm...\")\n",
+    "print(\"Fitting HMM with Baum-Welch algorithm (Rust implementation)...\")\n",
     "hmm.fit(returns, n_iterations=100, tolerance=1e-6)\n",
     "\n",
-    "print(\"\\nLearned Parameters:\")\n",
-    "print(f\"Transition Matrix:\\n{hmm.transition_matrix_}\")\n",
-    "print(f\"\\nEmission Means: {hmm.emission_means_}\")\n",
-    "print(f\"Emission Stds: {hmm.emission_stds_}\")"
+    "print(\"\\n✓ Training complete!\")\n",
+    "print(f\"\\nLearned Parameters:\")\n",
+    "print(f\"Transition Matrix:\")\n",
+    "for i, row in enumerate(hmm.transition_matrix_):\n",
+    "    print(f\"  State {i}: {[f'{p:.3f}' for p in row]}\")\n",
+    "print(f\"\\nEmission Means: {[f'{m:.4f}' for m in hmm.emission_means_]}\")\n",
+    "print(f\"Emission Stds: {[f'{s:.4f}' for s in hmm.emission_stds_]}\")"
    ]
   },
   {
@@ -163,14 +206,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "9fb3e502",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Viterbi decoding complete!\n",
+      "Predicted state distribution: [179 125 196]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Predict states using Viterbi\n",
+    "# Decode most likely state sequence using Viterbi algorithm (Rust)\n",
     "predicted_states = hmm.predict(returns)\n",
     "\n",
+    "print(f\"✓ Viterbi decoding complete!\")\n",
     "print(f\"Predicted state distribution: {np.bincount(predicted_states)}\")"
    ]
   },
@@ -328,16 +381,20 @@
     "# Generate larger dataset\n",
     "large_returns, _, _, _ = generate_regime_data(n_samples=5000)\n",
     "\n",
-    "# Time the fitting process\n",
-    "hmm_bench = HMM(n_states=3, random_state=42)\n",
+    "# Time the Rust fitting process\n",
+    "print(\"Benchmarking Rust HMM implementation...\")\n",
+    "hmm_bench = HMM(n_states=3)\n",
     "\n",
     "start = time.time()\n",
-    "hmm_bench.fit(large_returns, n_iterations=50)\n",
+    "hmm_bench.fit(large_returns, n_iterations=50, tolerance=1e-6)\n",
     "rust_time = time.time() - start\n",
     "\n",
-    "print(f\"Rust-accelerated fitting time: {rust_time:.3f} seconds\")\n",
-    "print(f\"For {len(large_returns)} observations with 50 iterations\")\n",
-    "print(f\"\\nEstimated pure Python time: ~{rust_time * 50:.1f}s (50-100x slower)\")"
+    "print(f\"\\n✓ Rust-accelerated fitting time: {rust_time:.3f} seconds\")\n",
+    "print(f\"  Dataset: {len(large_returns)} observations\")\n",
+    "print(f\"  Iterations: 50\")\n",
+    "print(f\"  States: 3\")\n",
+    "print(f\"\\n💡 Pure Python HMM libraries (hmmlearn) typically take 10-50× longer\")\n",
+    "print(f\"   Estimated Python time: ~{rust_time * 25:.1f}s\")"
    ]
   },
   {
@@ -361,8 +418,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "rhftlab",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

examples/notebooks/02_mcmc_tutorial.ipynb

@@ -2,10 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "dc5d5825",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OptimizR MCMC Module Loaded!\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -455,8 +463,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "rhftlab",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

examples/notebooks/03_differential_evolution_tutorial.ipynb

@@ -460,8 +460,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "rhftlab",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

examples/notebooks/04_real_world_applications.ipynb

@@ -2,10 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a2ac7765",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ All modules loaded successfully!\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -38,10 +46,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "bc76553e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated 730 days of market data\n",
+      "\n",
+      "Price range: $20,745 - $82,080\n",
+      "\n",
+      "Regime distribution:\n",
+      "regime_name\n",
+      "Bull       424\n",
+      "Bear       214\n",
+      "Neutral     92\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Return statistics:\n",
+      "Mean: 0.0004 (9.14% annual)\n",
+      "Std: 0.0400 (63.46% annual)\n",
+      "Sharpe: 0.14\n"
+     ]
+    }
+   ],
    "source": [
     "def generate_realistic_market_data(n_days=730, start_price=50000):\n",
     "    \"\"\"\n",
@@ -194,14 +224,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "d16db370",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting HMM to detect market regimes...\n",
+      "\n",
+      "✓ HMM fitted successfully!\n",
+      "\n",
+      "Learned Transition Matrix:\n",
+      "         State 0  State 1  State 2\n",
+      "State 0    0.339    0.247    0.414\n",
+      "State 1    0.252    0.441    0.306\n",
+      "State 2    0.459    0.215    0.326\n",
+      "\n",
+      "Emission Parameters:\n",
+      "         Mean Return  Volatility  Annual Return  Annual Vol\n",
+      "State 0      -0.0370      0.0297        -9.3169      0.4716\n",
+      "State 1       0.0019      0.0117         0.4693      0.1850\n",
+      "State 2       0.0369      0.0281         9.3038      0.4462\n"
+     ]
+    }
+   ],
    "source": [
     "# Fit HMM to detect regimes\n",
     "print(\"Fitting HMM to detect market regimes...\")\n",
-    "hmm = HMM(n_states=3, random_state=42)\n",
+    "hmm = HMM(n_states=3)\n",
     "hmm.fit(df_btc['return'].values, n_iterations=100, tolerance=1e-6)\n",
     "\n",
     "print(\"\\n✓ HMM fitted successfully!\")\n",
@@ -764,8 +816,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "rhftlab",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

examples/notebooks/05_performance_benchmarks.ipynb

@@ -2,10 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "59f619dc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⚠️  hmmlearn not installed. Installing...\n",
+      "✓ All modules loaded!\n",
+      "\n",
+      "============================================================\n",
+      "      BENCHMARK: OptimizR (Rust) vs Pure Python\n",
+      "============================================================\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -819,8 +832,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "rhftlab",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,