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"# CapiPort - PORTFOLIO OPTIMISATION"
]
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" Two things to consider for Portfolio Optimisation:\n",
" \n",
" 1) Minimising Risk\n",
" 2) Maximising Return"
]
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" Basic process of Portfolio Optimisation:\n",
" \n",
" 1) Select the Asset class to work on.\n",
" 1.1) Asset Class choosen - Equity (Stocks)\n",
" 2) Select the Companies which you want to use to build a Portfolio.\n",
" 2.1) Companies choosen - \n",
" 3) To try various Statistical Methods relating to Portfolio Optimisation.\n",
" 3.1) Method 1 - Result\n",
" 3.2) Method 2 - Result\n",
" 4) You will obtain Weigths or Percentages of Portfolio to invest.\n",
" 4.1) Method 1 - Weights\n",
" 4.2) Method 2 - Weights\n",
" 5) Testing the Portfolio for the future.\n",
" 5.1) Method 1 - Result\n",
" 5.2) Method 2 - Result\n",
" 6) Final Result"
]
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"source": [
"## <u>STEPS FOR IMPLEMENTING<u>\n",
"\n",
" 1) IMPORTING THE LIBRARIES\n",
" 2) TWEETS EXTRACTION FROM STOCKNET\n",
" 3) TWITTER DATA PRE-PROCESSING\n",
" 4) ZERO-SHOT SENTIMENT CLASSIFICATION\n",
" 5) FEATURE ENGINEERING OF TWEETS SENTIMENT VALUES\n",
" 5.1) Number of Tweets for each individual days\n",
" 5.2) Average of Emotion for each individual days\n",
" 5.3) Median of Sentiment for each Single Day\n",
" 5.4) Maximum Sentiment Value for each Single day\n",
" 5.5) Minimum Sentiment Value for Each Single Day\n",
" 5.6) Combining all the dataframes\n",
" 6) STOCK DATA FROM STOCKNET\n",
" 7) STOCK DATA AND FEATURE ENGINEERED SENTIMENT VALUES MERGING STEP\n",
" 9) WITH SENTIMENT\n",
" 9.1) DATASET PREPARATION FOR TRAINING\n",
" 9.2) TRAINING\n",
" 9.3) EVALUATING\n",
" 9.4) GRAPHS AND METRICS"
]
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