RESTful API is a very popular Web Service framework, which allow web developer to exchange json message through http call.
On the perspective of ease of use, REST is definitely easier than gRPC because gRPC requires the developer to define the message struct in the protocol buffer, and compile it to GO module with protoc compiler.
Market Index consists of a list of major companies stock price. There should be a correlation between their prices. Here I would like to use the Machine Learning Model (LSTM) to predict the market index with the historical data of certain stocks.
I use the python package yfinance to get the daily stock price. I downloaded 3-year figures including “Open”, “Close”, “High”, “Low”, and “Volume”
The target variable of the prediction is the Index ETL close price rate of return of the next day, which is defined as
target(t) = ( Close(t+1) - Close(t) )/Close(t) * 100%
Since the value…
Just learned the Oscillator of Moving average (OsMA) and RSI from the post. RSI is an useful indicator of trend and momentum. Inspired by that, I want to try to use that as the machine learning feature to predict the future stock return.
Relative Strength (RS) and Relative Strength Indicator (RSI) can be computed from the Moving Average (MA) of the historical positive price difference (%) divided by that of negative price difference (%).
RS = (MA of positive difference) / (MA of negative difference)
RSI = 100–100/(1+RS)
The price of the Financial Derivatives will depends on its underlying asset, so 2 financial derivatives with the same underlying asset should behave similarly, i.e. correlated. In this article, I will investigate the correlation of 2 GOLD ETFs: (GLD and IAU), and then develop the trading strategy using their correlation properties.
To know more what is ETF and Gold ETF, please refer to https://www.investopedia.com/articles/investing/032116/what-relationship-between-gold-and-gold-etfs-gld-iau.asp
GLD and IAU are 2 popular Gold ETFs held by different Trusts. Both will track the price of Gold very closely. Therefore, we will expect their rate of return are following similar pattern.
I just written the following article for the data science blogathon competition. It is about how to use Bayesian inference with Geometric Brownian Motion to simulate Stock Price. The implementation is by the Julia package “Turing.jl”. If you interested, you may take a look to support me.
(和合本) 我的 神必照他榮耀的豐富、在基督耶穌裏、使你們一切所需用的都充足。