Updated ReadME's and roadmap

2026-11-12 20:31:18 +00:00 · 2021-04-12 14:50:49 -04:00
parent 9045706a34
commit b5485f8f5e
4 changed files with 140 additions and 8 deletions
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -142,8 +142,13 @@
 * [ ] Add Brokers (td, webull, etc) (@jmaslek)
 * [ ] Refactoring
 * [ ] Summaries / tear sheets (@jmaslek)
-* [ ] Portfolio Optimization (@jmaslek)
+___  
+##Portfolio Optimization
+* [x] Basic Optimization through PyPortFolioOpt(@jmaslek) - [PR #329](https://github.com/DidierRLopes/GamestonkTerminal/pull/329)

+**NEXT**
+* [ ] Decide what features should be present
+* [ ] Allow for more custom optimization constrains
 ---

 ## FRED
--- a/gamestonk_terminal/README.md
+++ b/gamestonk_terminal/README.md
@@ -241,6 +241,21 @@ Command|Description|Brokers
 `hold`    | view net holdings across all logins

 &nbsp;
+## Portfolio Optimization [»](portfolio_optimization/README.md)
+command|desciription
+------|------
+`equal_weight`|Returns equal weights for all selected stocks
+`mkt_cap`| Returns weights that are weighted by market cap
+`div_yield`|Returns weights that are weighted by dividend yield
+`max_sharpe`| Returns portfolio of Efficient Frontier with maximum sharpe ratio
+`min_vol`| Returns portfolio on Efficient Frontier with minimum volatility
+`eff_risk`| Returns portfolio on Efficient Frontier that maximizes returns at a given risk
+`eff_ret`| Returns portfolio on Efficient Frontier that minimizes risk at a given return level
+`show_eff`|Plots random portfolios and shows the Efficient Frontier
+
+&nbsp;
+
+
 ## Cryptocurrency [»](cryptocurrency/README.md)

 Command|Description
--- a/gamestonk_terminal/main_helper.py
+++ b/gamestonk_terminal/main_helper.py
@@ -60,7 +60,9 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
    )
    print("   pa          portfolio analysis, \t\t supports: robinhood, alpaca, ally ")
    print("   crypto      cryptocurrencies, \t\t uses coingecko api")
-    print("   po          portfolio optimization, \t\t TBA")
+    print(
+        "   po          portfolio optimization, \t\t optimal portfolio weights from pyportfolioopt"
+    )

    if s_ticker:
        print(
--- a/gamestonk_terminal/portfolio_optimization/README.md
+++ b/gamestonk_terminal/portfolio_optimization/README.md
@@ -1,16 +1,126 @@
 # Portfolio Optimization

-This menu aims to discover optimal portfolios for selected stocks
+This menu aims to discover optimal portfolios for selected stocks.

+Currently we support two methods:

+* [Property Weighting](#weighting)
+    * Equal Weights
+    * Market Cap Weighting
+    * Divident Yield Weighting
+* [Mean-Variance-Optimization](#eff_front)
+    * Max sharpe ratio
+    * Minimum volatility
+    * Maximum returns at given risk level
+    * Minimum risk level at a given return 
+
+## Procedure
+There are three ways to load stocks to be analyzed. 
+* add
+* select
+* from ca menu
 ###add
-Adds tickers to current list for optimization
+Adds selected tickers to the menu to be considered
+
 ````
-usage: add ticker1,ticker2,...
+usage: add ticker1,ticker2,ticker3,...
 ````
-###optimize
-Runs optimization
+###select
+Clears current list and loads selected ticker
 ````
-usage: optimize
+usage: select ticker1,ticker2,ticker3,...
 ````

+### ca menu
+From the ca menu, the loaded ticker and the selected similar tickers can be loaded by entering the `> po` menu.
+
+From this `po` menu, you can return to `ca` using `ca`, which will keep your loaded stock, but reset the selected stocks from that menu.
+
+Once your stocks are listed, you can select one of the options.
+## Property weighted <a name="weighting"></a>
+* equal weights
+* market cap weighted
+* dividend yield weighted
+### equal weights
+````
+equal_weight
+````
+Returns `{"Ticker" : 1/# of tickers}`
+### market cap weighted
+````
+mkt_cap
+````
+Returns a dictionary where each weight is given as (Company Market Cap)/(Sum all included market caps)
+###dividened yield weighted
+````
+div_yield
+````
+Returns weights based on relative divident yield.
+
+## Mean Variance Optimization<a name="eff_front"></a>
+
+These approaches are based off of the efficient frontier approach, which is meant to solve the following optimization problem.
+
+<img src="https://latex.codecogs.com/svg.image?w^TS&space;w" title="w^TS w" />
+
+With constraints:
+
+<img src="https://latex.codecogs.com/svg.image?w^TR&space;>&space;R^*" title="w^TR > R^*" />
+
+\
+<img src="https://latex.codecogs.com/svg.image?w_1&plus;w_2&plus;...w_n&space;=&space;1" title="w_1+w_2+...w_n = 1" />
+
+Where S is the covariance matrix between stocks and R is the expected returns.  The condition that all weights add up to 1
+just implies that you want to have a net long portfolio (with no margin).  
+A long-short portfolio can have negative weights and usually wants to have everything add up to 0 for a market-neutral strategy.
+
+Currently, we do not allow for changing risk models or adding constraints.  If there is something spefic, please submit a feature request, or if you can
+write it, feel free to add a PR!
+
+All of our current implementations use the [PyPortFolioOpt](#https://pyportfolioopt.readthedocs.io/en/latest/index.html) package.
+
+### max_sharpe
+The sharpe ratio is defined as 
+
+(Mean Returns - Risk Free Rate)/(Standard Deviation of Returns)
+
+The usage is:
+````
+max_sharpe [-p PERIOD] 
+````
+* -p/--period Amount of time to retreive data from yfinance. Options are: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max and it defualts to 3mo.
+
+### min_vol
+This portfolio minimizes the total volatility, which also means it has the smallest returns among the efficient frontier.
+The usage is:
+````
+min_vol [-p PERIOD] 
+````
+* -p/--period Amount of time to retreive data from yfinance. Options are: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max and it defualts to 3mo.
+
+### eff_risk
+This portfolio maximizes the returns at a given risk tolerance
+The usage is:
+````
+eff_risk [-p PERIOD] [-r --risk RISK_LEVEL]
+````
+* -p/--period Amount of time to retreive data from yfinance. Options are: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max and it defualts to 3mo.
+* -r/--risk Risk tolerance.  5% is 0.05.
+
+### eff_ret
+This portfolio minimizes the risk at a given return level
+The usage is:
+````
+eff_ret [-p PERIOD] [-r --return]
+````
+* -p/--period Amount of time to retreive data from yfinance. Options are: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max and it defualts to 3mo.
+* -r/--return.  Desired return.  5% is 0.05.
+
+### show_eff
+This function plots random portfolios basd on their risk and returns and shows the efficient frontier.
+The usage is:
+````
+show_eff [-p PERIOD]  [-n N_PORTFOLIOS]
+````
+* -p/--period Amount of time to retreive data from yfinance. Options are: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max and it defualts to 3mo.
+* -n Number of portfolios to simulate.