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Iolcus Black-Litterman Portfolio Optimization
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Product.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Javascript, display\n",
"from ipywidgets import widgets\n",
"\n",
"#display(Javascript('IPython.notebook.execute_cells_below()'))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/thano.liaskas/.pyenv/versions/3.7.5/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import pandas as pd\n",
"import bqplot.pyplot as plt\n",
"import bqplot as bqp\n",
"import math as math\n",
"#import bqviz as bqv\n",
"import numpy as np\n",
"from numpy.linalg import inv\n",
"from collections import OrderedDict\n",
"import scipy\n",
"import ipywidgets as widgets\n",
"from scipy import optimize\n",
"\n",
"from ipywidgets import Button, Layout\n",
"\n",
"from ipywidgets import HBox, Label\n",
"\n",
"\n",
"#import bqwidgets as bqw\n",
"from ipywidgets import HBox, VBox, IntSlider, Text, Tab, FloatText, Label, Layout, FloatText, IntText, Checkbox, Button, FloatSlider, Dropdown, HTML # python library that contains items such labels, checkbox\n",
"from IPython.display import display # Required so that we are able to display widgets and other code at the same time otherwise widgets are supressed and not displayed.\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"\n",
"read_product_names = pd.read_excel ('https://github.com/liaskast/Jupyter-Notebook-Portfolio-Optimization/raw/master/Main/prices.xlsx',header=0,index_col=0, parse_dates= True, usecols=\"A:XFD\") # usecols: specifies which columns are read-in by the program. It should be column \"A\" until \"last_column + 1\".\n",
"product_names = read_product_names.columns.ravel() # product_names is an array that contains the names of the products inserted as input into our portfolio.\n",
"\n",
"data_frequency_choices = ['Daily data','Weekly data', 'Monthly data'] # contains all the possible choices for data frequency\n",
"data_frequency_checkbox = [0]*len(data_frequency_choices)\n",
"data_frequency_checkbox_default_value = [False, True, False]\n",
"\n",
"for data_freq_iterator in range(len(data_frequency_choices)):\n",
" style = {'description_width': 'initial'}\n",
" #initial_weight_from_sliders[product_names_iterator] = widgets.FloatSlider(value=1/len(product_names), layout={'width': '300'}, max=1, min=0, readout_format='.2%', step=0.2/100)\n",
" #display(HBox([VBox([Label(product_names[product_names_iterator]), initial_weight_from_sliders[product_names_iterator]])]))\n",
" data_frequency_checkbox[data_freq_iterator] = widgets.Checkbox(value=data_frequency_checkbox_default_value[data_freq_iterator],description=data_frequency_choices[data_freq_iterator],disabled=False,indent=False)\n",
" #display(data_frequency_checkbox[data_freq_iterator])\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set the Initial Weights"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
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"HBox(children=(VBox(children=(Label(value='EU Gov'), FloatSlider(value=0.14285714285714285, layout=Layout(widt…"
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"HBox(children=(VBox(children=(Label(value='EU Corps'), FloatSlider(value=0.14285714285714285, layout=Layout(wi…"
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"HBox(children=(VBox(children=(Label(value='Greek Gov'), FloatSlider(value=0.14285714285714285, layout=Layout(w…"
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"HBox(children=(VBox(children=(Label(value='US Equity'), FloatSlider(value=0.14285714285714285, layout=Layout(w…"
]
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"HBox(children=(VBox(children=(Label(value='EU Equity'), FloatSlider(value=0.14285714285714285, layout=Layout(w…"
]
},
"metadata": {},
"output_type": "display_data"
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"text/plain": [
"HBox(children=(VBox(children=(Label(value='EU Flot'), FloatSlider(value=0.14285714285714285, layout=Layout(wid…"
]
},
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"output_type": "display_data"
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"version_minor": 0
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"text/plain": [
"HBox(children=(VBox(children=(Label(value='Cash'), FloatSlider(value=0.14285714285714285, layout=Layout(width=…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from __future__ import print_function\n",
"import pandas as pd\n",
"import bqplot as bqp\n",
"import math as math\n",
"#import bqviz as bqv\n",
"import numpy as np\n",
"from numpy.linalg import inv\n",
"from collections import OrderedDict\n",
"import scipy\n",
"import ipywidgets as widgets\n",
"from scipy import optimize\n",
"\n",
"from ipywidgets import Button, Layout\n",
"\n",
"from ipywidgets import HBox, Label\n",
"\n",
"\n",
"#import bqwidgets as bqw\n",
"from ipywidgets import HBox, VBox, IntSlider, Text, Tab, FloatText, Label, Layout, FloatText, IntText, Checkbox, Button, FloatSlider, Dropdown, HTML # python library that contains items such labels, checkbox\n",
"from IPython.display import display # Required so that we are able to display widgets and other code at the same time otherwise widgets are supressed and not displayed.\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"\n",
"read_product_names = pd.read_excel ('https://github.com/liaskast/Jupyter-Notebook-Portfolio-Optimization/raw/master/Main/prices.xlsx',header=0,index_col=0, parse_dates= True, usecols=\"A:XFD\") # usecols: specifies which columns are read-in by the program. It should be column \"A\" until \"last_column + 1\".\n",
"product_names = read_product_names.columns.ravel() # product_names is an array that contains the names of the products inserted as input into our portfolio.\n",
"\n",
"initial_weight_from_sliders=[0]*len(product_names) # empty array that will contain the product names \n",
"\n",
"for product_names_iterator in range(len(product_names)):\n",
" style = {'description_width': 'initial'} # sets the style of the slider\n",
" initial_weight_from_sliders[product_names_iterator] = widgets.FloatSlider(value=1/len(product_names), layout={'width': '300'}, max=1, min=0, readout_format='.2%', step=0.2/100)\n",
" display(HBox([VBox([Label(product_names[product_names_iterator]), initial_weight_from_sliders[product_names_iterator]])])) # label contains the name displayed above the slider\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"text/plain": [
"Button(description='Update weights', style=ButtonStyle())"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Javascript, display\n",
"from ipywidgets import widgets\n",
"\n",
"def run_all(ev):\n",
" display(Javascript('IPython.notebook.execute_cells_below()'))\n",
"\n",
"button = widgets.Button(description=\"Update weights\")\n",
"button.on_click(run_all)\n",
"display(button)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Black-Litterman Portfolio Optimization"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
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"text/plain": [
"Figure(axes=[Axis(scale=OrdinalScale()), Axis(orientation='vertical', scale=LinearScale(), tick_format='0.2f')…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
},
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"VBox(children=(Tab(children=(VBox(children=(HBox(children=(HTML(value='<p style=\"color: white;\">Absolute Views…"
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"source": [
"# Import the Bloomberg Query Language (BQL) and bqfactor libraries\n",
"#import bql\n",
"#import bqport\n",
"# Import other data analytics and chatting libraries\n",
"\n",
"\n",
"from __future__ import print_function\n",
"import pandas as pd\n",
"import bqplot as bqp\n",
"import math as math\n",
"#import bqviz as bqv\n",
"import numpy as np\n",
"from numpy.linalg import inv\n",
"from collections import OrderedDict\n",
"import scipy\n",
"import ipywidgets as widgets\n",
"from scipy import optimize\n",
"\n",
"#import bqwidgets as bqw\n",
"from ipywidgets import HBox, VBox, IntSlider, Text, Tab, FloatText, Label, Layout, FloatText, IntText, Checkbox, Button, FloatSlider, Dropdown, HTML, interact # python library that contains items such labels, checkbox\n",
"from IPython.display import display # Required so that we are able to display widgets and other code at the same time otherwise widgets are supressed and not displayed.\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"#Loading animation\n",
"loading_html = HTML(\"\"\"\n",
" <div style=\"font-size:14px; color:lightskyblue;\">\n",
" <i class=\"fa fa-circle-o-notch fa-spin\"></i><span>&nbsp;Loading...</span>\n",
" </div>\"\"\")\n",
"\n",
"preload_box = HBox([loading_html])\n",
"preload_box\n",
"#display(preload_box) # Required to force the system to display the preload_box.\n",
"\n",
"#import numpy as np\n",
"#from bqplot import *\n",
"\n",
"size = 100\n",
"np.random.seed(0)\n",
"\n",
"x_data = range(size)\n",
"y_data = np.random.randn(size)\n",
"y_data_2 = np.random.randn(size)\n",
"y_data_3 = np.cumsum(np.random.randn(size) * 100.)\n",
"\n",
"x_ord = OrdinalScale()\n",
"y_sc = LinearScale()\n",
"\n",
"bar = Bars(x=np.arange(10), y=np.random.rand(10), scales={'x': x_ord, 'y': y_sc})\n",
"ax_x = Axis(scale=x_ord)\n",
"ax_y = Axis(scale=y_sc, tick_format='0.2f', orientation='vertical')\n",
"\n",
"Figure(marks=[bar], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025)\n",
"\n",
"\n",
"# Instantiate an object to interface with the BQL service\n",
"#bq = bql.Service() # object bq is defined #Requires Bloomberg's Database and is henceforth unaccessible to us.\n",
"\n",
"class Bloomberg_Object:\n",
" class data:\n",
" def day_to_day_total_return(self):\n",
" print(\"\")\n",
" def NAME(self):\n",
" print(\"\") \n",
"\n",
" class execute:\n",
" print(\"\")\n",
"\n",
" class univ:\n",
" print(\"\")\n",
"\n",
" class port:\n",
" def list_portfolios():\n",
" mytuple = (\"\", \"\", \"\")\n",
" return mytuple\n",
"\n",
"bq = Bloomberg_Object() \n",
"\n",
"#1 - Input the assets to portfolio.\n",
"security = OrderedDict()\n",
"#APOLIS\n",
"#security['1-5 years GILTS'] = 'SYB5 GY Equity'\n",
"#security['Cash'] = 'BNPIEGI LX Equity'\n",
"#security['Chinese Equity'] = 'SHSZ300 Index'\n",
"#security['Emerging Asia Equity'] = 'NDUEEGFA Index'\n",
"#security['EU High Yield Bonds'] = 'EUNW GY Equity'\n",
"#security['European Banks'] = 'SX7E Index'\n",
"#security['European Corp'] = 'EUN5 GR Equity'\n",
"#security['European Equity'] = 'SXXE Index'\n",
"#security['German Equity'] = 'DAX Index'\n",
"#security['Greek Equity'] = 'FTASE Index'\n",
"#security['Greek Govies'] = 'BEGCGA Index'\n",
"#security['Italian Equity'] = 'FTSEMIB Index'\n",
"#security['MSCI Info tech'] = 'NDWUIT Index'\n",
"#security['MSCI World'] = 'MACXUIGB Index'\n",
"#security['Spanish Equity'] = 'IBEX Index'\n",
"#security['US Equity'] = 'SPX Index'\n",
"#security['US High Yield Bonds'] = 'IBXXHYCT Index'\n",
"\n",
"#Benchmarks\n",
"#security['Euro Gov'] = 'Euro Gov'\n",
"#security['Greek Gov'] = 'Greek Gov'\n",
"#security['EU Corporate'] = 'EU Corporate'\n",
"#security['EU High Yield'] = 'EU HY'\n",
"#security['European Equities'] = 'Eur Eq'\n",
"#security['US Equities'] = 'US Eq'\n",
"#security['Cash'] = 'Cash'\n",
"\n",
"#Benchmarks 2 - TEA\n",
"#security['LEATTREU Index'] = 'EU Gov'\n",
"#security['LEC4TREU Index'] = 'EU Corps'\n",
"#security['BEGCGA Index'] = 'GR Corps'\n",
"#security['SXUSR Index\tUS'] = 'US Equity'\n",
"#security['SX5R Index'] = 'EU Equity'\n",
"#security['LEF1TREU Index'] = 'FRN EU'\n",
"#security['EUR001M Index'] = 'Cash'\n",
"\n",
"#Benchmarks 3 - TEA ETFs\n",
"#security['EUNH GY Equity'] = 'EU Gov'\n",
"#security['EUN5 GY Equity'] = 'EU Corps'\n",
"#security['LFGGBDR LX Equity'] = 'GR Corps'\n",
"#security['SPY US Equity'] = 'US Equity'\n",
"#security['SX5EEX GY Equity'] = 'EU Equity'\n",
"#security['FLOT FP Equity'] = 'FRN EU'\n",
"#security['PARSTEI LX Equity'] = 'Cash'\n",
"\n",
"#Bloomberg\n",
"#security['S&P 500'] = 'SPY US Equity'\n",
"#security['Real Estate'] = 'IYR US Equity'\n",
"#security['Russ 1K Val'] = 'IWD US Equity'\n",
"#security['Small Stocks'] = 'IWM US Equity'\n",
"#security['Commodities'] = 'DBC US Equity'\n",
"#security['GOLD'] = 'GLD US Equity'\n",
"#security['Russ 1K Gro'] = 'IWF US Equity'\n",
"#security['Bonds - Agg'] = 'AGG US Equity'\n",
"#security[\"Int'l Bonds\"] = 'BWX US Equity'\n",
"#security[\"High Yield\"] = 'HYG US Equity'\n",
"#security[\"US Treasuries\"] = 'GOVT US Equity'\n",
"#security[\"Emerging Mkts\"] = 'EEM US Equity'\n",
"\n",
"#2 - Input the weights of the portfolio.\n",
"#Original Weights that will provide us with the Implied returns. They only infulence the Implied returns' values and not the allocation policy. Even if originally we allocate 100% of our portfolio to one asset class the code will allocate according to the returns and will not be influenced by our weights, the weights only affect the implied returns.\n",
"\n",
"#Experimental Weights\n",
"# Weights for a portfolio Without Gilts and Chinese Equity.\n",
"#approximated_mkt_weight = [0.00957643167488187,0.010241765265639,0.398894134073001,0.00416351972379412,0.0967099088024052,0.0828703866165383,0.0235103219298358,0.0125595027532384,0.0120035820663699,0.0106296429781949,0.0202795023703381,0.035435880040154,0.00992384006540524,0.0311647410666334,0.0659143843855553]\n",
"#approximated_mkt_weight = [0.0112878580039961,0.164879596149528,0.00957643167488187,0.010241765265639,0.398894134073001,0.00416351972379412,0.0967099088024052,0.0828703866165383,0.0235103219298358,0.0125595027532384,0.0120035820663699,0.0106296429781949,0.0202795023703381,0.035435880040154,0.00992384006540524,0.0311647410666334,0.0659143843855553]\n",
"# Weights for a portfolio without Cash\n",
"#approximated_mkt_weight = [0.0112878580039961,0.00957643167488187,0.010241765265639,0.398894134073001,0.00416351972379412,0.0967099088024052,0.0828703866165383,0.0235103219298358,0.177439098902766,0.0120035820663699,0.0106296429781949,0.0202795023703381,0.035435880040154,0.00992384006540524,0.0311647410666334,0.0659143843855553]\n",
"\n",
"#print(len(approximated_mkt_weight)) # Prints the size of weights to confirm the right amount of products.\n",
"\n",
"# Original Apolis Weights\n",
"#approximated_mkt_weight = [0.0112878580039961,0.164879596149528,0.0248550020344915,0.00957643167488187,0.010241765265639,0.398894134073001,0.00416351972379412]\n",
"#approximated_mkt_weight = [0.0112878580039961,0.164879596149528,0.0248550020344915,0.00957643167488187,0.010241765265639,0.398894134073001,0.380265213]\n",
"#approximated_mkt_weight = [0.1465,0.2869,0.21863,0.214563,0.114563,0.11463,0.1146]\n",
"#approximated_mkt_weight = [0.01,0.16,0.024,0.00957,0.010241,0.39889,0.380265]\n",
"# Weights for Original Portfolio\n",
"#approximated_mkt_weight = [0.14,0.02, 0.15, 0.01,0.05,0.05,0.1, 0.05, 0.20, 0.05, 0.15, 0.03]\n",
"# Example Weights for attempt with 4 products\n",
"#approximated_mkt_weight = [0.14,0.02, 0.15, 0.01]\n",
"# TEA - weights\n",
"approximated_mkt_weight = [0.3,0.2, 0.15, 0.1,0.1,0.05,0.1]\n",
"#approximated_mkt_weight = [0.35,0.2, 0.15, 0.1,0.2]\n",
"\n",
"#approximated_mkt_weight = [0.3,0.2, 0.15, 0.1,0.1,0.05,0.1]\n",
"#approximated_mkt_weight = [0.3,0.3, 0.15, 0.1,0.1,0.05]\n",
"\n",
"#******************************************************************************** Reads in Input ****************************************************************************************************\n",
"#3 - Read in Asset Classes from Excel.\n",
"prices = pd.read_excel ('https://github.com/liaskast/Jupyter-Notebook-Portfolio-Optimization/raw/master/Main/prices.xlsx',header=1,index_col=0, parse_dates= True, usecols=\"A:XFD\") # usecols: specifies which columns are read-in by the program. It should be column \"A\" until \"last_column + 1\".\n",
"returns = prices.pct_change()\n",
"returns = returns.dropna()\n",
"\n",
"read_product_names = pd.read_excel ('https://github.com/liaskast/Jupyter-Notebook-Portfolio-Optimization/raw/master/Main/prices.xlsx',header=0,index_col=0, parse_dates= True, usecols=\"A:XFD\") # usecols: specifies which columns are read-in by the program. It should be column \"A\" until \"last_column + 1\".\n",
"product_names = read_product_names.columns.ravel() # product_names is an array that contains the names of the products inserted as input into our portfolio.\n",
"\n",
"#Benchmarks 3 - TEA ETFs\n",
"for iterator_product_names in range(len(product_names)):\n",
" security[product_names[iterator_product_names]] = product_names[iterator_product_names] \n",
" approximated_mkt_weight[iterator_product_names] = initial_weight_from_sliders[len(product_names)-1-iterator_product_names].value\n",
"\n",
"data_freq_multiplier_choices = [252, 52, 12] # all possible multiplier choices either for daily, weekly, monthly data\n",
"data_freq_multiplier = 0 # this variable will be what is multiplied with the returns in the 'solve_intial_opt_weight' optimization function\n",
"\n",
"for data_freq_iterator in range(len(data_frequency_choices)):\n",
" if data_frequency_checkbox[data_freq_iterator].value == True :\n",
" data_freq_multiplier = data_freq_multiplier_choices[data_freq_iterator] # assigns the right choice to the variable that will be multiplied depending on choice from the data frequency choice cell above \n",
"\n",
"dict_settings = OrderedDict()\n",
"dict_settings['security'] = security\n",
"dict_settings['weight'] = approximated_mkt_weight\n",
"\n",
"#rf = 0.015 # Original line of code - rf is the risk-free rate\n",
"rf = 0 # New line of code - rf is the risk-free rate\n",
"num_avail_ticker=len(dict_settings['security'])\n",
"#print(len(dict_settings['security'])) # prints the number of securities considered. This is used as a test to see whether the right portfolio is read as input.\n",
"uncertainty = 0.025 # tau is a scalar indicating the uncertainty in the CAPM (Capital Asset Pricing Model), this is a parameter for Black-Litterman\n",
"\n",
"\n",
"\n",
"import pickle\n",
"from collections import OrderedDict\n",
"from datetime import timedelta\n",
"\n",
"#Creates pickle file called 'settings_bl.pckl'. This file 'serializes' python Objects.\n",
"#try: \n",
" #f = open('settings_bl.pckl', 'rb') #**************************************************Issue?\n",
" #dict_settings = pickle.load(f)\n",
" #f.close() \n",
" \n",
"#except: # Defines Python Objects. \n",
" #dict_settings = OrderedDict()\n",
" #dict_settings['security'] = security\n",
" #dict_settings['weight'] = approximated_mkt_weight\n",
" #dict_settings['confidence'] = 0.8\n",
" #dict_settings['scalar'] = uncertainty\n",
" #dict_settings['usemktcap'] = False # Here we define the option to use the mkt cap as weighting if you choose index securities. We will not use!!!\n",
"\n",
"dict_settings['confidence'] = 0.8\n",
"#dict_settings['return target'] = 0.03\n",
"#return_slider=FloatSlider(value=dict_settings['return target'], description='Return Target', max=1, min=0, readout_format='.2%', layout={'margin':'20px 0px 0px 0px'},step=0.2/100,style={'description_width':'100PX'}) # slider for the return target\n",
"#return_slider.value = 0.03\n",
"dict_settings['scalar'] = uncertainty\n",
"dict_settings['usemktcap'] = False # Here we define the option to use the mkt cap as weighting if you choose index securities. We will not use!!!\n",
" \n",
"def save_settings(caller=None): # Reads from Button any changes to objects.\n",
" temp_sec, temp_weight = loadtickerfrominput()\n",
" dict_settings['security'] = temp_sec\n",
" dict_settings['weight'] = temp_weight\n",
" dict_settings['confidence'] = floattext_confidence.value\n",
" #dict_settings['return target'] = return_slider.value\n",
" dict_settings['usemktcap'] = check_usemktcap.value # If check_usemktcap.value == true then you are using the option to use the mkt cap as weighting if you choose index securities. We will not use!!!\n",
" f=open('settings_bl.pckl','wb')\n",
" pickle.dump(dict_settings, f)\n",
" f.close()\n",
" \n",
"def loadtickerfrominput(): # Reads from Button any changes to objects.\n",
" temp_ticker = []\n",
" temp_name = []\n",
" temp_weight = []\n",
" dict_missnametickers = OrderedDict()\n",
" flag_missingname = False\n",
" for n in range(num_avail_ticker): \n",
" if bool(list_sec_input[n+1].children[0].value):\n",
" temp_ticker.append(list_sec_input[n+1].children[0].value)\n",
" temp_name.append(list_sec_input[n+1].children[1].value)\n",
" if list_sec_input[n+1].children[1].value.strip() == '':\n",
" dict_missnametickers[list_sec_input[n+1].children[0].value] = n\n",
" flag_missingname = True\n",
" temp_weight.append(list_sec_input[n+1].children[2].value)\n",
" if flag_missingname:\n",
" df_name=bq_ref_data(dict_missnametickers.keys(),{'name':bq.data.NAME()}) # Gets 'name' of the security in the Tickers supplied as input. #******************** Calls function that calls Bloomberg's Database ************\n",
" for index,row in df_name.iterrows():\n",
" temp_name[dict_missnametickers[index]] = row['name']\n",
" temp_sec=OrderedDict(zip(temp_name,temp_ticker))\n",
" \n",
" return temp_sec, temp_weight\n",
"\n",
"def bq_ref_data(security,datafields):\n",
" # Generate the request using the sercurity variable and data item...i.e. the Tickers of financial instruments\n",
" #request = bql.Request(security, datafields) #******************** Directly TALKS TO Bloomberg's Database ************\n",
" #request = 1\n",
" #response = bq.execute(request) #******************** Directly TALKS TO Bloomberg's Database ************\n",
" #response = np.zeros(2,3)\n",
" #print(\"entered bq_ref_data\")\n",
" def merge(response): \n",
" return pd.concat([sir.df()[sir.name] for sir in response], axis=1)\n",
" result=merge(response)\n",
" return result\n",
"\n",
"def bq_series_data(security,datafields):\n",
" #request = bql.Request(security, datafields) #******************** Directly TALKS TO Bloomberg's Database ************\n",
" #request = 1\n",
" #response = bq.execute(request) #******************** Directly TALKS TO Bloomberg's Database ************\n",
" #print(\"entered bq_series_data\")\n",
" response = returns\n",
" #print(len(returns)) # for error correction: this prints the length of the returns array i.e. practically the number of rows of the excel file provided as input\n",
" #result = response[0].df().reset_index().pivot(index='DATE',columns='ID',values=response[0].name)[security]\n",
" return response\n",
"\n",
"# Portfolio Mean\n",
"def _port_mean(weights, expected_returns):\n",
" if((expected_returns.T * weights).sum()):#>return_slider.value): # This is where we place a bound on the return provided by the portfolio.\n",
" return((expected_returns.T * weights).sum())\n",
" else:\n",
" return 1\n",
"\n",
"# Portfolio Var\n",
"def _port_var(weights, risk_matrix):\n",
" return np.dot(np.dot(weights.T, risk_matrix), weights)\n",
"\n",
"# Portfolio Mean Var\n",
"def _port_mean_var(weights, expected_returns, risk_matrix):\n",
" return _port_mean(weights, expected_returns), _port_var(weights, risk_matrix)\n",
"\n",
"# Calculates Mean Var\n",
"def _find_mean_variance(weights, expected_returns, covar, return_target):\n",
" mean, var = _port_mean_var(weights, expected_returns, covar)\n",
" penalty = 10 * abs(mean-return_target)\n",
" return var + penalty\n",
"\n",
"# Solve for optimal portfolio weights\n",
"def solve_weights(R, C, rf, target_r = None):\n",
" n = len(R)\n",
" W = np.ones([n])/n # Start optimization with equal weights\n",
" b_ = [(0,1) for i in range(n)] # Bounds for decision variables\n",
" c_ = ({'type':'eq', 'fun': lambda W: sum(W)-1. }) # Constraints - weights must sum to 1\n",
" r = np.sum(R*W) if target_r is None else target_r\n",
" # 'target' return is the expected return on the market portfolio\n",
" optimized = scipy.optimize.minimize(_find_mean_variance, W, (R, C, r), method='SLSQP', constraints=c_, bounds=b_)\n",
" if not optimized.success:\n",
" raise BaseException(optimized.message)\n",
" return optimized.x \n",
"\n",
"# Constructs Efficient Frontier\n",
"def solve_for_frountier(R, C, rf):\n",
" frontier_mean, frontier_var , weights = [], [], []\n",
" for r in np.linspace(R.min(), R.max(), num=15):\n",
" weight = solve_weights(R, C, rf, r)\n",
" frontier_mean.append(r)\n",
" frontier_var.append(_port_var(weight, C))\n",
" weights.append(weight)\n",
" weights = pd.DataFrame(weights)\n",
" weights.index.name = 'portfolio'\n",
" frontier = pd.DataFrame([np.array(frontier_mean), np.sqrt(frontier_var)], index=['return', 'risk']).T\n",
" frontier.index.name = 'portfolio'\n",
" return frontier, weights\n",
"\n",
"# Initial Optimization of Weights...Implied Returns\n",
"def solve_intial_opt_weight():\n",
" global W_opt, frontier, f_weights, Pi, C, lmb, new_mean, W, R, mean_opt, var_opt\n",
" security = dict_settings['security']\n",
" univ = list(security.values())\n",
" datafields = OrderedDict()\n",
" #datafields['return'] = bq.data.day_to_day_total_return(start='-5y',per='m') # Datafields Parameter\n",
" day_to_day_return=bq_series_data(univ,datafields) #******************** Calls function that calls Bloomberg's Database ************\n",
" R = day_to_day_return.dropna().mean()*data_freq_multiplier # Input: 252 yearly # Description: R is the vector of expected returns in the \"Step-by-Step Guide...\" paper\n",
" C = day_to_day_return.cov()*data_freq_multiplier # Input: 252 yearly # Description: C is the covariance matrix i.e. Sigma in the \"Step-by-Step Guide...\" paper\n",
" \n",
" if dict_settings['usemktcap']: # This is the option to use the mkt cap as weighting if you choose index securities. We will not use!!!\n",
" datafields = OrderedDict()\n",
" datafields['Mkt Cap'] = bq.data.cur_mkt_cap(currency='usd') # Datafields Parameter\n",
" df_mkt_cap=bq_ref_data(univ,datafields) # Gets mkt_cap of the security in the Tickers. \n",
" W = np.array(df_mkt_cap/df_mkt_cap.sum()) # W is the market cap weight\n",
" else:\n",
" W = np.array(dict_settings['weight']).reshape(len(R),1)\n",
" \n",
" new_mean = _port_mean(W.T[0],R) # this variable is not used\n",
" new_var = _port_var(W,C) # this variable is not used\n",
"\n",
" lmb = 0.5/np.sqrt(W.T.dot(C).dot(W))[0][0] # Compute implied risk adversion coefficient: 1/(2*(wΣw))\n",
" Pi = np.dot(lmb * C, W) # Compute equilibrium excess returns\n",
"\n",
" frontier, f_weights = solve_for_frountier(Pi+rf, C, rf)\n",
" frontier['sharpe']=frontier['return']/frontier['risk']\n",
" f_weights.columns = R.keys()\n",
" # Solve for weights before incorporating views\n",
" W_opt = np.array(f_weights.iloc[frontier.loc[frontier['sharpe']==frontier['sharpe'].max()].index.values[0]])\n",
" mean_opt, var_opt = _port_mean_var(W_opt, Pi+rf, C) # calculate tangency portfolio\n",
" #print(mean_opt*100) # for error correction: prints the return provided by the Market Efficient Portfolio \n",
" #print(math.sqrt(var_opt)*100) # for error correction: prints the risk (i.e. cov) of the Market Efficient Portfolio\n",
" #print(\"\\n Initial Optimal Weights\")\n",
" #print(W_opt)\n",
" #return W_opt, frontier, f_weights, Pi, C\n",
" \n",
"\n",
"\n",
"solve_intial_opt_weight() # Here we call the Optimization function that returns the initial optimal weights.\n",
"# ************************************************************************************* GUI portion of the code that contains various labels,checkboxes etc. ***********************\n",
"\n",
"input_header = HBox([Label(value='Ticker', layout=Layout(width='120px',height='22px')), Label(value='Name of Asset', layout=Layout(width='120px',height='22px')), \n",
" Label(value='Weight',layout=Layout(width='120px',height='22px'))])\n",
"list_sec_input = [input_header]\n",
"lst_name = list(dict_settings['security'].keys())\n",
"lst_ticker = list(dict_settings['security'].values())\n",
"lst_weight = dict_settings['weight']\n",
"\n",
"# Checkbox that determines the mktcap option. Places in python object's 'check_usemktcap' value field a true or false value\n",
"check_usemktcap = Checkbox(description='Use Market Cap as Weight',value=dict_settings['usemktcap'], layout=Layout(min_width='15px'),style={'description_width':'initial'}) \n",
"\n",
"label_usemktcap = Label(value=' ',layout={'height':'22px'}) # MktCap Label\n",
"label_usemktcap2 = Label(value='(not recommended when using ETF as proxy)',layout=Layout(min_width='300px')) # MktCap Label\n",
"\n",
"# Option to load your own internal portfolio different from the original input.\n",
"\n",
"#port_dict = {x['name']: x['id'].split(':')[2].replace('-','U') + '-' + x['id'].split(':')[3] for x in bqport.list_portfolios()}\n",
"#port_dict = {x['name']: x['id'].split(':')[2].replace('-','U') + '-' + x['id'].split(':')[3] for x in bq.port.list_portfolios()}\n",
"#load_button = Button(description='Load members')\n",
"#portfolio_dropdown = Dropdown(description='Portfolio:',options=sorted(set(port_dict.keys())))\n",
"#load_members_hbox = HBox([portfolio_dropdown, load_button])\n",
"\n",
"\n",
"for n in range(num_avail_ticker):\n",
" text_name = Text(layout=Layout(width='120px'))\n",
" text_ticker = Text(layout=Layout(width='120px'))\n",
"\n",
" list_sec_input.append(HBox([text_ticker, text_name, FloatText(layout=Layout(width='120px'))]))\n",
" try:\n",
" if check_usemktcap.value:\n",
" list_sec_input[n+1].children[2].disabled = True\n",
" list_sec_input[n+1].children[2].layout.visibility = 'hidden'\n",
" list_sec_input[n+1].children[0].value = lst_ticker[n]\n",
" list_sec_input[n+1].children[1].value = lst_name[n] if lst_name[n] != '' else list_sec_input[n+1].children[0].data[0].split(\":\")[-1].strip() \n",
" list_sec_input[n+1].children[2].value = lst_weight[n]\n",
"\n",
" except:\n",
" pass\n",
" \n",
"def updateinputboxes(obj=None):\n",
" lst_name = list(dict_settings['security'].keys())\n",
" lst_ticker = list(dict_settings['security'].values())\n",
" for n in range(num_avail_ticker): \n",
" if list_sec_input[n+1].children[0].value.strip() != '' and list_sec_input[n+1].children[1].value.strip() == '':\n",
" list_sec_input[n+1].children[1].value = lst_name[n]\n",
" if check_usemktcap.value:\n",
" list_sec_input[n+1].children[2].disabled = True\n",
" list_sec_input[n+1].children[2].layout.visibility = 'hidden'\n",
" else:\n",
" list_sec_input[n+1].children[2].disabled = False\n",
" list_sec_input[n+1].children[2].layout.visibility = 'visible'\n",
"\n",
"check_usemktcap.observe(updateinputboxes, 'value')\n",
"\n",
"button_applysettings=Button(description = 'Apply Settings')\n",
"#button_reset=Button(description = 'Reset to No Views')\n",
"def onclickapplysettings(obj=None):\n",
" save_settings()\n",
" updateinputboxes()\n",
" solve_intial_opt_weight()\n",
" updateviewcontrol()\n",
" updatecontrolinui()\n",
" run_viewmodel({'new':0.})\n",
" \n",
"#display(button_reset)\n",
"#button_reset.on_click(onclickapplysettings)\n",
"button_applysettings.on_click(onclickapplysettings)\n",
"\n",
"#UI_sec_input = HBox([VBox(list_sec_input),VBox([load_members_hbox,label_usemktcap,check_usemktcap,label_usemktcap2,button_applysettings],layout={'margin':'0px 0px 0px 10px'})])\n",
"UI_sec_input = HBox([VBox(list_sec_input),VBox([label_usemktcap,check_usemktcap,label_usemktcap2,button_applysettings],layout={'margin':'0px 0px 0px 10px'})]) # Have taken load_members_hbox out because it requires a call to bloomberg's cde library which is not accessible to us.\n",
"\n",
"def on_click_load_portfolio(obj=None):\n",
" global df_portfolio_weight\n",
" #portfolio_univ = bq.univ.members(port_dict[portfolio_dropdown.value],type='PORT') #Requires Bloomberg's Database and is henceforth unaccessible to us.\n",
" #id_ = bq.data.id() #Requires Bloomberg's Database and is henceforth unaccessible to us.\n",
" #df_portfolio_weight = pd.concat([x.df() for x in bq.execute(bql.Request(portfolio_univ, [bq.data.name(),id_['Weights']/100]))],axis=1).reset_index() #******************** Directly TALKS TO Bloomberg's Database ************\n",
" df_portfolio_weight = approximated_mkt_weight\n",
" for x in range(1,num_avail_ticker+1):\n",
" if x - 1 < len(df_portfolio_weight):\n",
" list_sec_input[x].children[0].value = df_portfolio_weight.iloc[x-1,0]\n",
" list_sec_input[x].children[1].value = str(df_portfolio_weight.iloc[x-1,1])\n",
" list_sec_input[x].children[2].value = df_portfolio_weight.iloc[x-1,2]\n",
" else:\n",
" list_sec_input[x].children[0].value = ''\n",
" list_sec_input[x].children[1].value = ''\n",
" list_sec_input[x].children[2].value = 0\n",
"\n",
"#on_click_load_portfolio()\n",
"#load_button.on_click(on_click_load_portfolio)\n",
"\n",
"def run_viewmodel(change=None):\n",
" # VIEWS ON ASSET PERFORMANCE\n",
" \n",
" # for troubleshoot\n",
" global sub_a, sub_b, sub_c, sub_d, tau, omega, P, Q, Pi_new\n",
" list_security=list(dict_settings['security'].keys())\n",
" weights=OrderedDict()\n",
" \n",
" P=np.identity(len(dict_settings['security']))\n",
"\n",
" if isinstance(change['new'],float):\n",
" Q=[]\n",
" for n in range(len(dict_settings['security'])):\n",
" alpha = (list_slider[n].value - Pi[n][0]) * (confidence_list_slider[n].value) \n",
" #alpha = (list_slider[n].value - Pi[n][0]) * (floattext_confidence.value) # We supply the [primary 'view'] which is the Implied Return for each Asset Class.\n",
" # For example, we assume the first asset class will have implied return 6% as calculated from the historical data\n",
" # then, list_slider[n].value contains that 6% value. This is the [primary 'view'] for this asset class. \n",
" # Then on top of this [primary 'view'] we will add our own [portfolio manager 'view']. \n",
" # For example we believe that this asset class instead of 6% will have a 8% return. \n",
" # Hence, we move the 'slider' from the 6% position to the 8% position. Then on that 2% increase in excess return we multiply with the confidence on that view.\n",
" Q.append(alpha + Pi[n][0])\n",
"\n",
" \n",
" for relative_box in list_relative_controls:\n",
" sec1_pos = relative_box.children[0].value - 1\n",
" sec2_pos = relative_box.children[2].value - 1\n",
" if sec1_pos >= 0 and sec2_pos >= 0:\n",
" npselection = np.zeros(len(dict_settings['security']))\n",
" npselection[sec1_pos] = 1\n",
" npselection[sec2_pos] = -1\n",
" P = np.array(pd.DataFrame(P).append(pd.DataFrame(npselection).T))\n",
" #alpha = (relative_box.children[-1].value - (Pi[sec1_pos][0] - Pi[sec2_pos][0])) * (floattext_confidence.value)\n",
" alpha = (relative_box.children[-1].value - (Pi[sec1_pos][0] - Pi[sec2_pos][0])) * (confidence_list_slider[n].value) \n",
" Q.append(alpha + (Pi[sec1_pos][0] - Pi[sec2_pos][0]))\n",
" \n",
" Q=np.array([Q]).T \n",
" #tau = floattext_uncertainty.value \n",
" tau = 1/(5*12-len(list_security)) #tau is a scalar indicating the uncertainty. As specified in the Black-Litterman paper it should be anywhere between [0.01,0.05] and this formula guarantees that in the majority of cases. \n",
" #tau = 0.025 # this allows us to fix the uncertainty value to a pre-determined standard value, equal to the above formula when 20 asset classes are supplied as input..\n",
" omega = np.dot(np.dot(np.dot(tau, P), C), P.T)# omega represents uncertanity of views implied uncertainty from market parameters.\n",
"\n",
" # Compute equilibrium excess returns taking into account views on assets\n",
" sub_a = inv(np.dot(tau, C))\n",
" sub_b = np.dot(np.dot(P.T, inv(omega)), P)\n",
" sub_c = np.dot(inv(np.dot(tau, C)), Pi)\n",
" sub_d = np.dot(np.dot(P.T, inv(omega)), Q)\n",
" Pi_new = np.dot(inv(sub_a + sub_b), (sub_c + sub_d)) \n",
" # Perform a mean-variance optimization taking into account views \n",
" new_frontier, new_f_weights = solve_for_frountier(Pi_new + rf, C, rf)\n",
" new_frontier['sharpe']=new_frontier['return']/new_frontier['risk']\n",
" # Solve for weights before incorporating views\n",
" new_weights = np.array(new_f_weights.iloc[new_frontier.loc[new_frontier['sharpe']==new_frontier['sharpe'].max()].index.values[0]])\n",
"\n",
" leverage = np.sum(abs(new_weights))\n",
" initial_weights = np.array(approximated_mkt_weight)\n",
" weights['Initial Weights']=initial_weights\n",
" weights['Opt Portfolio']=W_opt[::-1]\n",
" weights['Opt Portfolio with View']=new_weights[::-1]\n",
" \n",
" output_df = pd.DataFrame(weights, index=list_security[::-1])\n",
" output_df.to_excel('output.xlsx')\n",
"\n",
" mean, var = _port_mean_var(new_weights[::-1], Pi_new + rf, C)\n",
" scatt.x = [np.sqrt(var_opt)]\n",
" scatt.y = [mean_opt]\n",
" scatt_view.x = [np.sqrt(var) ]\n",
" scatt_view.y = [mean]\n",
" \n",
" bar.x = list_security[::-1]\n",
" bar.y = [weights[col] for col in weights]\n",
"\n",
" labels_initial.y = list_security[::-1]\n",
" labels_initial.x = weights['Initial Weights']\n",
" labels_initial.text = np.around(weights['Initial Weights']*100,1)\n",
"\n",
" labels_opt.y = list_security[::-1]\n",
" labels_opt.x = weights['Opt Portfolio']\n",
" labels_opt.text = np.around(weights['Opt Portfolio']*100,1)\n",
"\n",
" labels_opt_view.y = list_security[::-1]\n",
" labels_opt_view.x = weights['Opt Portfolio with View']\n",
" labels_opt_view.text = np.around(weights['Opt Portfolio with View']*100,1)\n",
" \n",
" line.x = frontier['risk']\n",
" line.y = [frontier['return'],new_frontier['return']]\n",
" \n",
"\n",
"\n",
"#floattext_confidence = FloatSlider(description='Confidence Level on Views', value=dict_settings['confidence'],style={'description_width':'initial'}, readout_format='.2%', max=1, min=0,\n",
" # layout={'margin':'20px 0px 0px 0px'}, step=0.5/100)\n",
" \n",
"#floattext_confidence.observe(run_viewmodel) \n",
"\n",
"#return_slider.observe(run_viewmodel)\n",
"\n",
"#sv = pd.Series(np.sqrt(np.diag(Pi.T.dot(C.dot(Pi))).astype(float)), index=C.index)\n",
"def updateviewcontrol():\n",
" global UI_viewcontrol, list_slider, confidence_list_slider, security_list, list_relative_controls, floattext_uncertainty\n",
" \n",
" list_slider=[]\n",
" confidence_list_slider=[]\n",
" security_list=[]\n",
" #list_security=list(dict_settings['security'].keys()) # Original line\n",
" list_security=list(dict_settings['security'].values()) # Changed the name next to sliders to ticker name.\n",
" for n in range(len(dict_settings['security'])):\n",
" #temp_slider=FloatSlider(value=Pi[n], description=list_security[n], continuous_update=False, max=0.2, min=-0.2, readout_format='.2%', step=0.2/100,style={'description_width':'100PX'})\n",
" temp_slider=FloatSlider(value=Pi[n], description='View on Asset', max=0.2, min=-0.2, readout_format='.2%', step=0.2/100,style={'description_width':'100PX'}) #Slider Specficiations. Pi[n] contains the [primary 'view'] and is the starting point of the slider. max,min specify the maximum amount of return you can spec on an asset class. description=list_security[n]--> contains the name attached to the left of each slider.\n",
" #display(temp_slider) # this command was required to forcefully display sliders when bqplot did not use to work. It is no longer required as Bqplot now works on the jupyter notebook.\n",
" temp_slider.observe(run_viewmodel)\n",
" list_slider.append(temp_slider)\n",
"\n",
" floattext_confidence = FloatSlider(description='Confidence of View', value=0.8,style={'description_width':'150PX'}, readout_format='.2%', max=1, min=0,\n",
" step=0.5/100)\n",
" floattext_confidence.observe(run_viewmodel) \n",
" confidence_list_slider.append(floattext_confidence)\n",
"\n",
" security_label = Label(list_security[n])\n",
" security_label.observe(run_viewmodel)\n",
" security_list.append(security_label)\n",
"\n",
"\n",
" list_relative_controls=[]\n",
" sec_dropdown_options = OrderedDict(zip(['None']+list(dict_settings['security'].keys()),range(len(dict_settings['security'])+1)))\n",
" \n",
" for n in range(3):\n",
" dropdown1 = Dropdown(options=sec_dropdown_options,layout={'width':'100px'})\n",
" dropdown2 = Dropdown(options=sec_dropdown_options,layout={'width':'100px'})\n",
" label = Label(value='over',layout={'width':'30px'})\n",
" float_value = FloatSlider(description='by', value=0, readout_format='.2%', max=0.2, min=0,\n",
" style={'description_width':'initial'}, step=0.1/100,layout={'width':'200px'})\n",
" float_value.observe(run_viewmodel)\n",
" relative_box = HBox([dropdown1,label,dropdown2,float_value])\n",
" list_relative_controls.append(relative_box)\n",
" \n",
" header_abs_html = HTML('<p style=\"color: white;\">{}</p>'.format('Absolute Views'))\n",
" header_rel_html = HTML('<p style=\"color: white;\">{}</p>'.format('Relative Views'), layout={'margin':'20px 0px 0px 0px'})\n",
" UI_viewcontrol = [header_abs_html, VBox([HBox([VBox(security_list), VBox(list_slider), VBox(confidence_list_slider)])]), header_rel_html]#, VBox(list_relative_controls), VBox([floattext_confidence])]\n",
" #display(VBox([HBox([VBox(security_list), VBox(list_slider), VBox(confidence_list_slider)])]))\n",
"\n",
"\n",
"\n",
"def updatecontrolinui():\n",
" UI_model.children[0].children = UI_viewcontrol\n",
"\n",
"updateviewcontrol()\n",
"\n",
"# END OF ************************************************************************************* GUI portion of the code that contains various labels,checkboxes etc. ***********************\n",
"# START OF ************************************************************************************************************** Build bar charts (use of bqp) ***********************\n",
"\n",
"x_ord = bqp.OrdinalScale()\n",
"y_sc = bqp.LinearScale()\n",
"y_sc.max = 1\n",
"\n",
"#Plot #1 i.e. Creation of the bar plot\n",
"\n",
"bar = bqp.Bars(x=[], \n",
" y=[], \n",
" scales={'x': x_ord, 'y': y_sc},\n",
" orientation=\"horizontal\", display_legend=True, labels=['Initial Weights','Mkt Efficient Portfolio (Max Sharpe)','Efficient Portfolio with Views (BL)'], #orientation decides whether the bars are horizontal or vertical\n",
" colors=['#4fa110','#1B84ED','#F39F41'], opacities=[0.8, 0.9, 1],\n",
" type='grouped')\n",
"\n",
"#bar.type='grouped'\n",
"bar.tooltip = bqp.Tooltip(fields=['y'], labels=['Weight of Asset'], formats=['.3f']) #this displays the weight placed on each asset.\n",
"\n",
"ax_x = bqp.Axis(scale=x_ord, orientation=\"vertical\")\n",
"ax_y = bqp.Axis(scale=y_sc, label='Weight')\n",
"\n",
"#fig_bar = bqp.Figure(marks=[bar], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025, \n",
" #layout=Layout(width='800px'), legend_location='top-right', \n",
" #fig_margin={'top':20, 'bottom':30, 'left':80, 'right':20})\n",
"\n",
"#fig_bar = bqp.Figure(marks=[bar,bar_labels], axes=[ax_x, ax_y], padding_x=0.025, padding_y=0.025, \n",
"# layout=Layout(width='600px'), legend_location='top', \n",
"# fig_margin={'top':20, 'bottom':30, 'left':110, 'right':20}) \n",
"\n",
"\n",
"ax_x = bqp.Axis(scale=x_ord, orientation=\"vertical\", color = 'Black')\n",
"ax_y = bqp.Axis(scale=y_sc, tick_format='0.2f', color = 'White')\n",
"\n",
"labels_initial = bqp.Label(y=[], x=[], scales={'y': x_ord, 'x': y_sc}, #contain value of weights but only for the labels not the bars.\n",
" x_offset=2,y_offset = -17, \n",
" text=[], colors=['#4fa110'], \n",
" default_size=14, update_on_move=True)\n",
"\n",
"labels_opt = bqp.Label(y=[], x=[], scales={'y': x_ord, 'x': y_sc}, \n",
" x_offset=2,y_offset = 0, \n",
" text=[], colors=['#1B84ED'], \n",
" default_size=14, update_on_move=True)\n",
"\n",
"labels_opt_view = bqp.Label(y=[], x=[], scales={'y': x_ord, 'x': y_sc}, \n",
" x_offset=2,y_offset = 17, \n",
" text=[], colors=['#F39F41'], \n",
" default_size=14, update_on_move=True)\n",
"\n",
"fig_bar = bqp.Figure(marks=[labels_opt_view,labels_opt,labels_initial,bar], axes=[ax_x, ax_y], padding_x=0.005, padding_y=0.025, \n",
" layout=Layout(width='800px'), legend_location='top', legend_text={'font-size': 13}, legend_style = {'stroke-width': 0},\n",
" fig_margin={'top':20, 'bottom':30, 'left':80, 'right':20}) \n",
"\n",
"#Plot #2 i.e. the efficient froniter plot\n",
"\n",
"x_lin = bqp.LinearScale()\n",
"y_lin = bqp.LinearScale()\n",
"#x_lin.max = 0.2 # controls the length of the x-axis\n",
"#y_lin.max = 0.1 # controls the length of the y-axis\n",
"\n",
"x_ax = bqp.Axis(label='risk', scale=x_lin, grid_lines='solid')\n",
"x_ay = bqp.Axis(label='return', scale=y_lin, orientation='vertical', grid_lines='solid')\n",
"\n",
"def_tt = bqp.Tooltip(fields=['x', 'y'], formats=['.3f', '.3f']) \n",
"\n",
"scatt = bqp.Scatter(x=[],y=[], scales={'x': x_lin, 'y': y_lin}, tooltip=def_tt,\n",
" display_legend=True, labels=['Efficient Portfolio'], colors=['#1B84ED'])\n",
"scatt_view = bqp.Scatter(x=[],y=[], scales={'x': x_lin, 'y': y_lin}, tooltip=def_tt,\n",
" display_legend=True, labels=['Efficient Portfolio with Views Portfolio'], colors=['#F39F41'])\n",
"\n",
"line = bqp.Lines(x=[], y=[], scales={'x': x_lin, 'y': y_lin}, display_legend=True, labels=['Mkt Efficient Portfolio','Efficient Portfolio with Views'], colors=['#1B84ED','#F39F41'])\n",
"\n",
"fig_line = bqp.Figure(marks=[line], axes=[x_ax, x_ay], \n",
" legend_location='top-left', layout=Layout(width='800px'), legend_text={'font-size': 13}, legend_style = {'stroke-width': 0},\n",
" fig_margin={'top':20, 'bottom':30, 'left':80, 'right':20})\n",
"run_viewmodel({'new':0.})\n",
"UI_model=VBox([HBox(UI_viewcontrol,layout=Layout(width='1000px')),HBox([fig_bar]),HBox([fig_line])]) # specifies the length of the lhs of the interface\n",
"#display(HBox([fig_bar]))\n",
"#display(VBox([HBox([VBox(UI_viewcontrol), VBox([fig_bar])])]))\n",
"#display(VBox([HBox([VBox(security_list), VBox(list_slider), VBox(confidence_list_slider)])])) \n",
"#UI_model = HBox([loading_html])\n",
"\n",
"#interact(updateviewcontrol, UI_model)\n",
"\n",
"# END OF ************************************************************************************************************** Build bar charts (use of bqp) ***********************\n",
"\n",
"tab = Tab()\n",
"#tab.children = [UI_model, UI_sec_input]\n",
"tab.children = [UI_model]\n",
"tab.set_title(0, 'Portfolio Optimizer')\n",
"#tab.set_title(1, 'Settings')\n",
"#tab.set_title(2, 'Reference Data')\n",
"\n",
"def updatedseclist(obj=None):\n",
" if obj['old'] == 1:\n",
" save_settings()\n",
" solve_intial_opt_weight()\n",
" run_viewmodel({'new':0.})\n",
"\n",
"# START OF ************************************************************************************************************** (use of bqcde) *********************** \n",
"\n",
"#import bqcde #Requires Bloomberg's Database and is henceforth unaccessible to us. AND BELOW...interal portfolio thing\n",
"from datetime import date\n",
"def upload_to_cde(obj):\n",
" obj.description = 'Uploading...'\n",
" #lmb_2 = 0.5/np.sqrt(np.asarray([W_opt]).dot(C).dot(np.asarray([W_opt]).T))[0][0]\n",
" #Pi_2 = np.dot(lmb_2 * C, np.asarray([W_opt]).T)\n",
" df = pd.DataFrame({'ID':list(dict_settings['security'].values()),'Equil Return': [x[0] for x in Pi], 'BL Return':[x[0] for x in Pi_new] })\n",
" as_of_date = date.today()\n",
" upload_dict = {'Equil Return':'UD_EQUIL_RETURN','BL Return':'UD_BL_RETURN'}\n",
" #pd.DataFrame({'ID':list(dict_settings['security'].keys()),'Equil Return': [x[0] for x in Pi]}).set_index('ID')\n",
" try:\n",
" df = df.rename(columns=upload_dict)\n",
" list_to_upload = list(upload_dict.values())\n",
" #fs = bqcde.get_fields(mnemonics=list_to_upload)\n",
" df['AS_OF_DATE'] = as_of_date\n",
" df['AS_OF_DATE'] = pd.to_datetime(df['AS_OF_DATE'])\n",
" #bqcde.write(fields=fs, dataframe=df.fillna(\"N/A\"))\n",
" except Exception as e:\n",
" print(e)\n",
" obj.description = 'Upload to CDE'\n",
"\n",
"#upload_to_cde()\n",
"#button = Button(description='Upload to CDE')\n",
"#button.on_click(upload_to_cde)\n",
"\n",
"# END OF ************************************************************************************************************** (use of bqcde) *********************** \n",
"\n",
"preload_box.children = []\n",
"#VBox([button,tab])\n",
"VBox([tab])\n",
"\n",
"#[Open Weight](output.xlsx)\n",
"\n",
"#for slider in list_slider:\n",
" #print(slider.description, \": \", slider.value) #supressing this print out \n"
]
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Raw
requirements.txt
View raw

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