modified script

.ipynb_checkpoints/[Reference and Explanation Notebook] Calculate Intrinsic Value of a Stock-checkpoint.ipynb

@@ -2033,9 +2033,9 @@
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        "                            \n",
-       "var gd = document.getElementById('7de251e6-2ea0-4b8a-ab13-60a1a9bc3e13');\n",
+       "var gd = document.getElementById('eb1ffaee-a2d5-4a0c-b00e-bdf7be39f5e6');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -2617,8 +2617,7 @@
     "    if not EPS_growth_5Y.isdigit():\n",
     "        EPS_growth_5Y = finviz_data['EPS next Y']\n",
     "EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate\n",
-    "#EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate\n",
-    "long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate\n",
+    "long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y/2, 3)  #  Around long term inflation rate, conservative estimate\n",
     "\n",
     "shares_outstanding = finviz_data['Shs Outstand']\n",
     "\n",
@@ -3617,9 +3616,9 @@
        }
       },
       "text/html": [
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+       "<div>                            <div id=\"9d31dd7b-1811-4fd7-8cea-0ad548111ab0\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9d31dd7b-1811-4fd7-8cea-0ad548111ab0\")) {                    Plotly.newPlot(                        \"9d31dd7b-1811-4fd7-8cea-0ad548111ab0\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=Cash Flow<br>Year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"Cash Flow\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Cash Flow\",\"offsetgroup\":\"Cash 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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Forecasted Year\"},\"type\":\"category\",\"tickangle\":-90},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Free Cash Flows\"}},\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"title\":{\"text\":\"AAPL Projected Free Cash Flows\"},\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('7f2c3727-2087-4d13-a490-a5f262b38fc1');\n",
+       "var gd = document.getElementById('9d31dd7b-1811-4fd7-8cea-0ad548111ab0');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -3946,8 +3945,7 @@
     "        if not EPS_growth_5Y.isdigit():\n",
     "            EPS_growth_5Y = finviz_dict['EPS next Y']\n",
     "    EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate\n",
-    "    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate\n",
-    "    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate\n",
+    "    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Around long term inflation rate, conservative estimate\n",
     "    shares_outstanding = finviz_dict['Shs Outstand']\n",
     "    beta = finviz_dict['Beta']\n",
     "    current_price = finviz_dict['Price']\n",
@@ -4084,7 +4082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -4493,7 +4491,7 @@
        " -336.9241027809666)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }

[Reference and Explanation Notebook] Calculate Intrinsic Value of a Stock.ipynb

@@ -2617,8 +2617,7 @@
     "    if not EPS_growth_5Y.isdigit():\n",
     "        EPS_growth_5Y = finviz_data['EPS next Y']\n",
     "EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate\n",
-    "#EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate\n",
-    "long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate\n",
+    "long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y/2, 3)  #  Around long term inflation rate, conservative estimate\n",
     "\n",
     "shares_outstanding = finviz_data['Shs Outstand']\n",
     "\n",
@@ -3946,8 +3945,7 @@
     "        if not EPS_growth_5Y.isdigit():\n",
     "            EPS_growth_5Y = finviz_dict['EPS next Y']\n",
     "    EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate\n",
-    "    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate\n",
-    "    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate\n",
+    "    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Around long term inflation rate, conservative estimate\n",
     "    shares_outstanding = finviz_dict['Shs Outstand']\n",
     "    beta = finviz_dict['Beta']\n",
     "    current_price = finviz_dict['Price']\n",
@@ -4084,7 +4082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -4493,7 +4491,7 @@
        " -336.9241027809666)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }

app.py

@@ -138,8 +138,7 @@ def parse_finviz_dict(finviz_dict):
         if not EPS_growth_5Y.isdigit():
             EPS_growth_5Y = finviz_dict['EPS next Y']
     EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate
-    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate
-    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate
+    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y/2, 3)  # Around long term inflation rate, conservative estimate
     shares_outstanding = finviz_dict['Shs Outstand']
     beta = finviz_dict['Beta']
     current_price = finviz_dict['Price']

app.py.bak

@@ -138,8 +138,8 @@ def parse_finviz_dict(finviz_dict):
         if not EPS_growth_5Y.isdigit():
             EPS_growth_5Y = finviz_dict['EPS next Y']
     EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate
-    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate
-    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate
+    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Around long term inflation rate, conservative estimate
+    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y/2, 3)  # Around long term inflation rate, conservative estimate
     shares_outstanding = finviz_dict['Shs Outstand']
     beta = finviz_dict['Beta']
     current_price = finviz_dict['Price']
@@ -298,12 +298,12 @@ with gr.Blocks() as app:
         gr.HTML("<h3>https://finviz.com/</h3>")
         
     with gr.Row():
-            EPS_growth_5Y = gr.Text(label="EPS Next 5Y (Estimated EPS growth for next 5 years)")
+            EPS_growth_5Y = gr.Text(label="EPS Next 5Y (estimated EPS growth for next 5 years)")
             EPS_growth_6Y_to_10Y = gr.Text(label="EPS growth for 6th to 10th year (estimated as half of above)")
             long_term_growth_rate = gr.Text(label="Long Term Growth Rate (estimated as half of above or 3%, whichever is lower)")  
     
     with gr.Row():
-        beta = gr.Text(label="Beta (Measures volatility of stock)")
+        beta = gr.Text(label="Beta (measures volatility of stock)")
         discount_rate = gr.Text(label="Discount Rate (estimated from beta)") 
         shares_outstanding = gr.Text(label="Shares Outstanding")