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efficiency frontier + stock_picking files #12

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Feb 13, 2020
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efficiency frontier + stock_picking files #12

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efficiency frontier + stock_picking files
kitas91 Feb 13, 2020
439aacf
Merge branch 'master' into lucas
kitas91 Feb 13, 2020
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156 changes: 156 additions & 0 deletions .ipynb_checkpoints/efficiency_frontier-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import scipy.optimize as sco\n",
"plt.style.use('fivethirtyeight')\n",
"np.random.seed(777)\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"tickers = ['HST', 'NWS', 'GPC', 'KO', 'DISCK']\n",
"data_df = pd.DataFrame()\n",
"\n",
"for t in tickers:\n",
" data_df[t] = wb.DataReader(a, data_source = 'yahoo', start = '2010-1-1')['Adj Close']"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"returns = data_df.pct_change()\n",
"returns.dropna(inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def portfolio_annualised_performance(weights, mean_returns, cov_matrix):\n",
" returns = np.sum(mean_returns*weights ) *252\n",
" std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)\n",
" return std, returns"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"def random_portfolios(num_portfolios, mean_returns, cov_matrix, risk_free_rate):\n",
" results = np.zeros((3,num_portfolios))\n",
" weights_record = []\n",
" for i in xrange(num_portfolios):\n",
" weights = np.random.random(4)\n",
" weights /= np.sum(weights)\n",
" weights_record.append(weights)\n",
" portfolio_std_dev, portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix)\n",
" results[0,i] = portfolio_std_dev\n",
" results[1,i] = portfolio_return\n",
" results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev\n",
" return results, weights_record"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate):\n",
" results, weights = random_portfolios(num_portfolios,mean_returns, cov_matrix, risk_free_rate)\n",
" \n",
" max_sharpe_idx = np.argmax(results[2])\n",
" sdp, rp = results[0,max_sharpe_idx], results[1,max_sharpe_idx]\n",
" max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=table.columns,columns=['allocation'])\n",
" max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]\n",
" max_sharpe_allocation = max_sharpe_allocation.T\n",
" \n",
" min_vol_idx = np.argmin(results[0])\n",
" sdp_min, rp_min = results[0,min_vol_idx], results[1,min_vol_idx]\n",
" min_vol_allocation = pd.DataFrame(weights[min_vol_idx],index=table.columns,columns=['allocation'])\n",
" min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]\n",
" min_vol_allocation = min_vol_allocation.T\n",
" \n",
" print (\"-\"*80)\n",
" print (\"Maximum Sharpe Ratio Portfolio Allocation\\n\")\n",
" print (\"Annualised Return:\", round(rp,2))\n",
" print (\"Annualised Volatility:\", round(sdp,2))\n",
" print (\"\\n\")\n",
" print (max_sharpe_allocation)\n",
" print (\"-\"*80)\n",
" print (\"Minimum Volatility Portfolio Allocation\\n\")\n",
" print (\"Annualised Return:\", round(rp_min,2))\n",
" print (\"Annualised Volatility:\", round(sdp_min,2))\n",
" print (\"\\n\")\n",
" print (min_vol_allocation)\n",
" \n",
" plt.figure(figsize=(10, 7))\n",
" plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)\n",
" plt.colorbar()\n",
" plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')\n",
" plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')\n",
" plt.title('Simulated Portfolio Optimization based on Efficient Frontier')\n",
" plt.xlabel('annualised volatility')\n",
" plt.ylabel('annualised returns')\n",
" plt.legend(labelspacing=0.8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
107 changes: 107 additions & 0 deletions .ipynb_checkpoints/stock_picking-checkpoint.ipynb
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156 changes: 156 additions & 0 deletions efficiency_frontier.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,156 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import scipy.optimize as sco\n",
"plt.style.use('fivethirtyeight')\n",
"np.random.seed(777)\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"tickers = ['HST', 'NWS', 'GPC', 'KO', 'DISCK']\n",
"data_df = pd.DataFrame()\n",
"\n",
"for t in tickers:\n",
" data_df[t] = wb.DataReader(a, data_source = 'yahoo', start = '2010-1-1')['Adj Close']"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"returns = data_df.pct_change()\n",
"returns.dropna(inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def portfolio_annualised_performance(weights, mean_returns, cov_matrix):\n",
" returns = np.sum(mean_returns*weights ) *252\n",
" std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)\n",
" return std, returns"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"def random_portfolios(num_portfolios, mean_returns, cov_matrix, risk_free_rate):\n",
" results = np.zeros((3,num_portfolios))\n",
" weights_record = []\n",
" for i in xrange(num_portfolios):\n",
" weights = np.random.random(4)\n",
" weights /= np.sum(weights)\n",
" weights_record.append(weights)\n",
" portfolio_std_dev, portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix)\n",
" results[0,i] = portfolio_std_dev\n",
" results[1,i] = portfolio_return\n",
" results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev\n",
" return results, weights_record"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate):\n",
" results, weights = random_portfolios(num_portfolios,mean_returns, cov_matrix, risk_free_rate)\n",
" \n",
" max_sharpe_idx = np.argmax(results[2])\n",
" sdp, rp = results[0,max_sharpe_idx], results[1,max_sharpe_idx]\n",
" max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=table.columns,columns=['allocation'])\n",
" max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]\n",
" max_sharpe_allocation = max_sharpe_allocation.T\n",
" \n",
" min_vol_idx = np.argmin(results[0])\n",
" sdp_min, rp_min = results[0,min_vol_idx], results[1,min_vol_idx]\n",
" min_vol_allocation = pd.DataFrame(weights[min_vol_idx],index=table.columns,columns=['allocation'])\n",
" min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]\n",
" min_vol_allocation = min_vol_allocation.T\n",
" \n",
" print (\"-\"*80)\n",
" print (\"Maximum Sharpe Ratio Portfolio Allocation\\n\")\n",
" print (\"Annualised Return:\", round(rp,2))\n",
" print (\"Annualised Volatility:\", round(sdp,2))\n",
" print (\"\\n\")\n",
" print (max_sharpe_allocation)\n",
" print (\"-\"*80)\n",
" print (\"Minimum Volatility Portfolio Allocation\\n\")\n",
" print (\"Annualised Return:\", round(rp_min,2))\n",
" print (\"Annualised Volatility:\", round(sdp_min,2))\n",
" print (\"\\n\")\n",
" print (min_vol_allocation)\n",
" \n",
" plt.figure(figsize=(10, 7))\n",
" plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)\n",
" plt.colorbar()\n",
" plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')\n",
" plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')\n",
" plt.title('Simulated Portfolio Optimization based on Efficient Frontier')\n",
" plt.xlabel('annualised volatility')\n",
" plt.ylabel('annualised returns')\n",
" plt.legend(labelspacing=0.8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
7 changes: 0 additions & 7 deletions ffm_regression.ipynb
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Expand Up @@ -510,13 +510,6 @@
"source": [
"ffm_data.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
107 changes: 107 additions & 0 deletions stock_picking.ipynb
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