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Merge branch 'shyam' of https://github.com/bhanuprasanna527/CapiPort into shyam

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Files changed (2) hide show
  1. Notebooks/Untitled.ipynb +156 -9
  2. main.py +15 -3
Notebooks/Untitled.ipynb CHANGED
@@ -86,8 +86,92 @@
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  "outputs": [
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- "text/plain": " 0 1 2 3 \\\ncount 2062 2062 2062 2062 \nunique 2062 2044 2060 2056 \ntop 20 Microns Ltd. 20MICRONS ₹1.14 -4.16% ₹8.8/₹3.79 ₹128.05 Crs \nfreq 1 4 2 2 \n\n 4 Name Ticker \ncount 2062 2062 2062 \nunique 125 2061 2062 \ntop Pharmaceuticals Gallantt Ispat Ltd. 20MICRONS \nfreq 105 2 1 ",
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- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>Name</th>\n <th>Ticker</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>2062</td>\n <td>2062</td>\n <td>2062</td>\n <td>2062</td>\n <td>2062</td>\n <td>2062</td>\n <td>2062</td>\n </tr>\n <tr>\n <th>unique</th>\n <td>2062</td>\n <td>2044</td>\n <td>2060</td>\n <td>2056</td>\n <td>125</td>\n <td>2061</td>\n <td>2062</td>\n </tr>\n <tr>\n <th>top</th>\n <td>20 Microns Ltd. 20MICRONS</td>\n <td>₹1.14 -4.16%</td>\n <td>₹8.8/₹3.79</td>\n <td>₹128.05 Crs</td>\n <td>Pharmaceuticals</td>\n <td>Gallantt Ispat Ltd.</td>\n <td>20MICRONS</td>\n </tr>\n <tr>\n <th>freq</th>\n <td>1</td>\n <td>4</td>\n <td>2</td>\n <td>2</td>\n <td>105</td>\n <td>2</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  },
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  "metadata": {},
@@ -111,7 +195,9 @@
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  "data": {
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- "text/plain": "2062"
 
 
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  "execution_count": 5,
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  "metadata": {},
@@ -150,8 +236,68 @@
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  "outputs": [
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  {
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  "data": {
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- "text/plain": " 0 1 \\\n610 Gallantt Ispat Ltd.\\n ... ₹64.15 -0.77% \n611 Gallantt Ispat Ltd.\\n ... ₹216.94 +1.33% \n\n 2 3 4 Name Ticker \n610 ₹76/₹44.64 ₹1807.11 Crs Iron & Steel Gallantt Ispat Ltd. GALLISPAT \n611 ₹236.4/₹49.54 ₹5235.8 Crs Iron & Steel Gallantt Ispat Ltd. GALLANTT ",
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- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>Name</th>\n <th>Ticker</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>610</th>\n <td>Gallantt Ispat Ltd.\\n ...</td>\n <td>₹64.15 -0.77%</td>\n <td>₹76/₹44.64</td>\n <td>₹1807.11 Crs</td>\n <td>Iron &amp; Steel</td>\n <td>Gallantt Ispat Ltd.</td>\n <td>GALLISPAT</td>\n </tr>\n <tr>\n <th>611</th>\n <td>Gallantt Ispat Ltd.\\n ...</td>\n <td>₹216.94 +1.33%</td>\n <td>₹236.4/₹49.54</td>\n <td>₹5235.8 Crs</td>\n <td>Iron &amp; Steel</td>\n <td>Gallantt Ispat Ltd.</td>\n <td>GALLANTT</td>\n </tr>\n </tbody>\n</table>\n</div>"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "execution_count": 7,
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  "metadata": {},
@@ -179,12 +325,13 @@
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  "metadata": {
@@ -203,7 +350,7 @@
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  "name": "python",
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  "nbconvert_exporter": "python",
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  "pygments_lexer": "ipython3",
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- "version": "3.10.13"
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  }
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  "nbformat": 4,
 
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  "outputs": [
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  "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " }\n",
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+ "\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <th>3</th>\n",
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+ " <th>4</th>\n",
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+ " <th>Ticker</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>count</th>\n",
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+ " <td>2062</td>\n",
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+ " <td>2062</td>\n",
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+ " <td>2062</td>\n",
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+ " <td>2062</td>\n",
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+ " <td>2062</td>\n",
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+ " <td>2062</td>\n",
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+ " </tr>\n",
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+ " <th>unique</th>\n",
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+ " <td>2062</td>\n",
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+ " <td>2044</td>\n",
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+ " <td>2060</td>\n",
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+ " <td>2056</td>\n",
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+ " <td>125</td>\n",
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+ " <td>2061</td>\n",
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+ " <td>2062</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>top</th>\n",
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+ " <td>20 Microns Ltd. 20MICRONS</td>\n",
141
+ " <td>₹1.14 -4.16%</td>\n",
142
+ " <td>₹8.8/₹3.79</td>\n",
143
+ " <td>₹128.05 Crs</td>\n",
144
+ " <td>Pharmaceuticals</td>\n",
145
+ " <td>Gallantt Ispat Ltd.</td>\n",
146
+ " <td>20MICRONS</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>freq</th>\n",
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+ " <td>1</td>\n",
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+ " <td>4</td>\n",
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+ " <td>2</td>\n",
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+ " <td>2</td>\n",
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+ " <td>105</td>\n",
155
+ " <td>2</td>\n",
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+ " <td>1</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " 0 1 2 3 \\\n",
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+ "count 2062 2062 2062 2062 \n",
165
+ "unique 2062 2044 2060 2056 \n",
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+ "top 20 Microns Ltd. 20MICRONS ₹1.14 -4.16% ₹8.8/₹3.79 ₹128.05 Crs \n",
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+ "freq 1 4 2 2 \n",
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+ "\n",
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+ " 4 Name Ticker \n",
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+ "count 2062 2062 2062 \n",
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+ "unique 125 2061 2062 \n",
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+ "top Pharmaceuticals Gallantt Ispat Ltd. 20MICRONS \n",
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+ "freq 105 2 1 "
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+ ]
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  },
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  "execution_count": 4,
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  "metadata": {},
 
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  "outputs": [
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  {
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  "data": {
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+ "text/plain": [
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+ "2062"
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+ ]
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  },
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  "execution_count": 5,
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  "metadata": {},
 
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  "outputs": [
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  {
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  "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>0</th>\n",
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+ " <th>1</th>\n",
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+ " <th>2</th>\n",
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+ " <th>3</th>\n",
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+ " <th>4</th>\n",
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+ " <th>Name</th>\n",
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+ " <th>Ticker</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>610</th>\n",
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+ " <td>Gallantt Ispat Ltd.\\n ...</td>\n",
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+ " <td>₹64.15 -0.77%</td>\n",
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+ " <td>₹76/₹44.64</td>\n",
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+ " <td>₹1807.11 Crs</td>\n",
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+ " <td>Iron &amp; Steel</td>\n",
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+ " <td>Gallantt Ispat Ltd.</td>\n",
276
+ " <td>GALLISPAT</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>611</th>\n",
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+ " <td>Gallantt Ispat Ltd.\\n ...</td>\n",
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+ " <td>₹216.94 +1.33%</td>\n",
282
+ " <td>₹236.4/₹49.54</td>\n",
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+ " <td>₹5235.8 Crs</td>\n",
284
+ " <td>Iron &amp; Steel</td>\n",
285
+ " <td>Gallantt Ispat Ltd.</td>\n",
286
+ " <td>GALLANTT</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " 0 1 \\\n",
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+ "610 Gallantt Ispat Ltd.\\n ... ₹64.15 -0.77% \n",
295
+ "611 Gallantt Ispat Ltd.\\n ... ₹216.94 +1.33% \n",
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+ "\n",
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+ " 2 3 4 Name Ticker \n",
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+ "610 ₹76/₹44.64 ₹1807.11 Crs Iron & Steel Gallantt Ispat Ltd. GALLISPAT \n",
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+ "611 ₹236.4/₹49.54 ₹5235.8 Crs Iron & Steel Gallantt Ispat Ltd. GALLANTT "
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+ ]
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  },
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  "execution_count": 7,
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  "metadata": {},
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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+ "id": "e9274e3c3011e6fc",
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  "metadata": {
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  "collapsed": false
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+ "outputs": [],
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+ "source": []
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  }
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  ],
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  "metadata": {
 
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  "name": "python",
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  "nbconvert_exporter": "python",
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  "pygments_lexer": "ipython3",
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+ "version": "3.10.12"
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  }
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  },
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  "nbformat": 4,
main.py CHANGED
@@ -3,6 +3,9 @@ import numpy as np
3
  import yfinance as yf
4
  import streamlit as st
5
  import plotly.graph_objects as go
 
 
 
6
 
7
  with open(r"style/style.css") as css:
8
  st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
@@ -152,9 +155,13 @@ if num_tick > 1:
152
  ## Create an Array to store the Sharpe Ratios as they are generated
153
  sharpe_arr = np.zeros(num_of_port)
154
 
155
- ## Let's start the Monte Carlo Simulation
 
 
156
 
157
- for ind in range(num_of_port):
 
 
158
  ## Let's first Calculate the Weights
159
  weig = np.array(np.random.random(num_tick))
160
  weig = weig / np.sum(weig)
@@ -170,10 +177,15 @@ if num_tick > 1:
170
 
171
  ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
172
  sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]
 
 
 
 
 
173
 
174
  ## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio
175
  sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]
176
-
177
  ## Create a Data Frame using above, then Transpose it
178
  sim_df = pd.DataFrame(data=sim_data).T
179
 
 
3
  import yfinance as yf
4
  import streamlit as st
5
  import plotly.graph_objects as go
6
+ import time
7
+ import sys
8
+
9
 
10
  with open(r"style/style.css") as css:
11
  st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
 
155
  ## Create an Array to store the Sharpe Ratios as they are generated
156
  sharpe_arr = np.zeros(num_of_port)
157
 
158
+ ## Track Progress with a Bar
159
+ progress_text = "Operation in progress. Please wait."
160
+ my_bar = st.progress(0, text=progress_text)
161
 
162
+ ## Let's start the Monte Carlo Simulation
163
+ for ind in range(num_of_port): # Corrected the range to iterate from 0 to num_of_port
164
+ time.sleep(0.001)
165
  ## Let's first Calculate the Weights
166
  weig = np.array(np.random.random(num_tick))
167
  weig = weig / np.sum(weig)
 
177
 
178
  ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
179
  sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]
180
+ if ind % 100 == 0:
181
+ print(ind)
182
+ my_bar.progress((ind + 1) / num_of_port, text=progress_text)
183
+ # clear progress bar
184
+ my_bar.empty()
185
 
186
  ## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio
187
  sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]
188
+
189
  ## Create a Data Frame using above, then Transpose it
190
  sim_df = pd.DataFrame(data=sim_data).T
191