# Building your Long Term Portfolio using Unsupervised ML with Community Detection Analysis in Python & R

We finally get to the **Portfolio Building** where we can try to beat the SPY average returns Year over Yer (YoY). The whole code base can be found here:

To build the portfolio based on the correlation of stock prices in the S&P 500, we will use network centrality as a measure to understand how the stocks are better integrated with one and other.

Using this diagram below you can understand the process better.

We have done everything to required from data gathering, preprocessing to applying unsupervised clustering model to create the communities of stocks in **Part** **1** and **Part 2** of this story. We will now be using the **Louvain** method as discussed before to calculate centrality of the stocks using 4 very important measures.

For this we built an algorithm that applies equal weightage to the score of the four centralities we considered while building our portfolio. The 4 being:

Degree Centrality — that assigns a score based simply on the number of links held by each node.

Betweenness Centrality — which assigns a score based on the number of times a node lies on the shortest path between other nodes.

Closeness Centrality — which scores each node based on their ‘closeness’ to all other nodes in the network.

Eigen Centrality — which measures a node’s influence based on the number of links it has to other nodes in the network.

Together these centralities give us a score that help us determine the overall influence of a stock on the network. We can see these with the code below:

stocks_cross_corr, _, _ = calculate_corr(df_stock_prices,1, len(df_stock_prices), 'pearson')

stocks_cross_corr = stocks_cross_corr[1]cor_thresold = 0.7

G = build_graph(stocks_cross_corr, cor_thresold)

partition = community.best_partition(G)

modularity = community.modularity(partition, G)

values = [partition.get(node) for nodeinG.nodes()]

plt.figure(figsize=(10,10))

nx.draw_spring(G, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=False)print(modularity)

print("Total number of Communities=", len(G.nodes()))

dict_betwenness_centrality = nx.betweenness_centrality(G)

dict_degree_centrality = nx.degree_centrality(G)

dict_closeness_centrality = nx.closeness_centrality(G)

dict_eigenvector_centrality = nx.eigenvector_centrality(G)

print("dict_degree_centrality: ", dict_degree_centrality)

print("dict_closeness_centrality: ", dict_closeness_centrality)

print("dict_eigenvector_centrality: ", dict_eigenvector_centrality)

print("dict_betweenness_centrality: ", dict_betwenness_centrality)

## Once the four types of centralities are obtained we will build our own portfolio determining algorithm using these centralities in an equal weightage sense. To do that we combine the dictionary data structure of our 4 centralties and use a for loop to iterate through each stock and add the four different centralities to create a final centrality metric. Since we are using equal weightage the formula would use a simple sum and not be any assigned any weights.

*#Portfolio Formula: *

c_dict = dict([(k, [dict_betwenness_centrality[k], dict_eigenvector_centrality[k], dict_degree_centrality[k], dict_closeness_centrality[k] ]) for k **in** dict_betwenness_centrality])

*#print(c_dict) *

C_total = {}

for key **in** c_dict:

C_total[key] = sum(c_dict[key])

print("The Centrality total for stocks are:", C_total)

newDict = dict(filter(lambda elem: elem[1] > 0, C_total.items()))

print("Stocks greater than 0.3 centrality are",newDict)

print(len(newDict))

df_centrality = pd.DataFrame(list(newDict.items()),columns = ['Symbol','Centrality'])

df_centrality.sort_values(by='Centrality', ascending=False)

*#df_centrality.head(20)*

*#type(df_centrality['Centrality'])*

df_centrality.to_csv('centrality_of_stocks_0.7cor.csv',index=False)

As seen from the image above **Apple (AAPL)** located on the 7th index has a total centrality score of 0.126. This gives us a good idea on how the stock is placed in our network and community, in terms of degrees of connections, betweenness and closeness amongst nodes along with their eigenvalues

Using the centrality score from our algorithm, we further filtered the stocks by sector so that we could bring diversification in our portfolio as well.Making use of the tidyquant package in **R** we then compared our portfolio to the S&P500’s in the time frame of monthly returns. We were happy to see that our index over the last 5 years has beaten the benchmark S&P 500 and has indeed provided a better return on investment percentage.

The whole code base for the portfolio making can be found here:

We will start by loading the required packages in **R**

*# This R environment comes with many helpful analytics packages installed*

*# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats*

*# For example, here's a helpful package to load*

library(tidyverse)

library(tidyquant)

library(jsonlite)

library(tidyverse)

library(readr)

library(igraph)

library(dplyr)

library(lubridate)

library(data.table)

library(Quandl) *# metapackage of all tidyverse packages*

*# Input data files are available in the read-only "../input/" directory*

*# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory*

list.files(path = "../input")

*# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" *

*# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session*

We will then load the dataset that we made using different correlation thresholds as shown below like *0.5, 0.6 and 0.7*. This will be used in a variety of examples as shown below to make your portfolio. These **datasets** can be found on kaggle here:

*#DataLoad *

stockprices <- read.csv("../input/usstockprices/stocks_price_final.csv")

centrality0.5 <- read.csv("../input/centrality-of-stocks-network-analysis/centrality_of_stocks_0.5cor.csv")

centrality0.6 <- read.csv("../input/centrality-of-stocks-network-analysis/centrality_of_stocks_0.6cor.csv")

centrality0.7 <- read.csv("../input/centrality-of-stocks-network-analysis/centrality_of_stocks_0.7cor.csv")

We will then do some data cleaning by joining stocks with their centralities. Once that is done, we can begin the portfolio generation and comparison to SPY depending on whichever correlation you want of the stocks

#Merges Centtraility values with other potential useful information like market cap and sector for further filtering

stocks <- stockprices[!duplicated(stockprices$symbol), ]# removes duplicate symbols

Index0.5cor <- merge(stocks, centrality0.5, by.x = "symbol", by.y = "Symbol")

Index0.6cor <- merge(stocks, centrality0.6, by.x = "symbol", by.y = "Symbol")

Index0.7cor <- merge(stocks, centrality0.7, by.x = "symbol", by.y = "Symbol")#head(Index0.6cor)

IndexGet0.5 <- as.character(Index0.5cor$symbol)

IndexGet0.6 <- as.character(Index0.6cor$symbol)

IndexGet0.7 <- as.character(Index0.7cor$symbol)#Portfolio Testing##0.5 correlation centrality prices

stockindex0.5 <- tq_get(IndexGet0.5, get="stock.prices", from = "2015-07-01",warnings = FALSE,

stringsAsFactors = FALSE) %>%

group_by(symbol) %>%

tq_transmute(select=adjusted,

mutate_fun=periodReturn,

period="monthly",

col_rename = "monthly_return")

stockindex0.5##0.6 correlation centrality prices

stockindex0.6 <- tq_get(IndexGet0.6, get="stock.prices", from = "2015-07-01",warnings = FALSE,

stringsAsFactors = FALSE) %>%

group_by(symbol) %>%

tq_transmute(select=adjusted,

mutate_fun=periodReturn,

period="monthly",

col_rename = "monthly_return")##0.7 correlation centrality prices

stockindex0.7 <- tq_get(IndexGet0.7, get="stock.prices", from = "2015-07-01",warnings = FALSE,

stringsAsFactors = FALSE) %>%

group_by(symbol) %>%

tq_transmute(select=adjusted,

mutate_fun=periodReturn,

period="monthly",

col_rename = "monthly_return")##Base Portfolio to compare

baseline_returns_monthly <- "SPY" %>%

tq_get(get = "stock.prices",

from = "2015-07-01", warnings = FALSE,stringsAsFactors = FALSE) %>%

tq_transmute(select = adjusted,

mutate_fun = periodReturn,

period = "monthly",

col_rename = "spy_monthly_return")

baseline_returns_monthly

Portfolio calculation from above method

`portfolio_returns_monthly0.5 <- stockindex0.5 %>%`

tq_portfolio(assets_col = symbol,

returns_col = monthly_return,

col_rename = "portfolio-monthly")

portfolio_returns_monthly0.6 <- stockindex0.6 %>%

tq_portfolio(assets_col = symbol,

returns_col = monthly_return,

col_rename = "portfolio-monthly")

portfolio_returns_monthly0.7 <- stockindex0.7 %>%

tq_portfolio(assets_col = symbol,

returns_col = monthly_return,

col_rename = "portfolio-monthly")

*#Portfolio Compare*

stock0.5indexVSSPY <- left_join(portfolio_returns_monthly0.5,

baseline_returns_monthly,

by = "date")

stock0.6indexVSSPY <- left_join(portfolio_returns_monthly0.6,

baseline_returns_monthly,

by = "date")

stock0.7indexVSSPY <- left_join(portfolio_returns_monthly0.7,

baseline_returns_monthly,

by = "date")

*#stock0.5indexVSSPY*

ggplot(stock0.5indexVSSPY) + geom_line(aes(x = `date`, y = `portfolio-monthly`), color = "blue")+ geom_line(aes(x = `date`, y = `spy_monthly_return`), color = "red")

ggplot(stock0.6indexVSSPY) + geom_line(aes(x = `date`, y = `portfolio-monthly`), color = "blue")+ geom_line(aes(x = `date`, y = `spy_monthly_return`), color = "red")

ggplot(stock0.7indexVSSPY) + geom_line(aes(x = `date`, y = `portfolio-monthly`), color = "blue")+ geom_line(aes(x = `date`, y = `spy_monthly_return`), color = "red")

**Playground here to optimize your portfolio! Choose the correlation you want from the datasets above and then further optimize your portfolio by filtering the stocks by sector and their centrality value. This will help you create a niche list of stocks that could potentially beat the**** SPY returns YoY of 8.93%****.**

*##PLAYGROUND - LETS DO TRIAL AND ERROR HERE TO BEAT THE SPY GRAPH in RETURNS*

centrality_filter <- Index0.6cor

*#filter(Centrality > 0.8 & Centrality < 0.5)*

centrality_filter

IndexGet0.6 <- as.character(centrality_filter$symbol)

stockindex0.6 <- tq_get(IndexGet0.6, get="stock.prices", from = "2015-07-01",warnings = FALSE,

stringsAsFactors = FALSE) %>%

group_by(symbol) %>%

tq_transmute(select=adjusted,

mutate_fun=periodReturn,

period="monthly",

col_rename = "monthly_return")

portfolio_returns_monthly0.6 <- stockindex0.6 %>%

tq_portfolio(assets_col = symbol,

returns_col = monthly_return,

col_rename = "portfolio-monthly")

stock0.6indexVSSPY <- left_join(portfolio_returns_monthly0.6,

baseline_returns_monthly,

by = "date")

ggplot(stock0.6indexVSSPY) + geom_line(aes(x = `date`, y = `portfolio-monthly`), color = "blue")+ geom_line(aes(x = `date`, y = `spy_monthly_return`), color = "red")

This is the code I wrote to achieve a portfolio that could beat the SPY returns over a 5 year period from July 1st 2015 to July 22nd 2020

`centrality_filter <- Index0.5cor %>%`

filter((sector == "Health Care" & Centrality > 0.4) | (sector == "Technology" & Centrality > 0.42) | (sector == "Consumer Services" & Centrality > 0.49) | (sector == "Finance" & Centrality > 0.5) | (sector == "Transportation" & Centrality > 0.5) | (sector == "Capital Goods" & Centrality > 0.35) | (sector == "Miscellaneous" & Centrality > 0.5) | (sector == "Basic Industries" & Centrality > 0.39) | (sector == "Public Utilities" & Centrality > 0.36) | (sector == "Consumer Durables" & Centrality > 0.3)| (sector == "Consumer Non-Durables" & Centrality > 0.25))

centrality_filter

IndexGet0.5 <- as.character(centrality_filter$symbol)

stockindex0.5 <- tq_get(IndexGet0.5, get="stock.prices", from = "2015-07-01", till = "2020-07-22",warnings = FALSE,

stringsAsFactors = FALSE) %>%

group_by(symbol) %>%

tq_transmute(select=adjusted,

mutate_fun=periodReturn,

period="monthly",

col_rename = "monthly_return")

portfolio_returns_monthly0.5 <- stockindex0.5 %>%

tq_portfolio(assets_col = symbol,

returns_col = monthly_return,

col_rename = "portfolio-monthly")

stock0.5indexVSSPY <- left_join(portfolio_returns_monthly0.5,

baseline_returns_monthly,

by = "date")

stock0.5indexVSSPY$`portfolio-monthly` <- stock0.5indexVSSPY$`portfolio-monthly` * 100

stock0.5indexVSSPY$spy_monthly_return <- stock0.5indexVSSPY$spy_monthly_return * 100

stock0.5indexVSSPY <- stock0.5indexVSSPY[-c(14), ]

returns <- ggplot(stock0.5indexVSSPY) + geom_line(aes(x = `date`, y = `portfolio-monthly`), color = "blue")+ geom_line(aes(x = `date`, y = `spy_monthly_return`), color = "red") + ggtitle("Portfolio vs S&P 500 Returns over 5 years") + labs(y="Returns Percenatge", x = "Date")

plot(returns)

From the plot above we can visualize a more volatile trend for my portfolio which is the blue line as comp to red which’s the SPY returns. But overall, the blue line has a higher Return on Investment (ROI) percentage over 5 years.

The centralities as shown in the above code were chosen based on my predictions for future trends in terms of what sectors will do well and become important to society as a service.