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fx_lstm_1

Project in Python and R for the course Research Seminar in the Master of Banking and Finance at the University of St. Gallen: Creation of a FOREX prediction model (CHF/USD trading pair). LSTM model to predict FOREX (in Jupyter) and baseline models (i.e. ARIMA and Random Walk) in R and Excel.

fx_lstm_2_pair-trading

Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM

fx_lstm_3-models-for-pair-trading

LSTM Models for Pair Trading Strategy

fx_lstm_4-pairs-trading

Using LSTM to create pairs trading strategy using options

fx_lstm_5_pairs-trading

fx_lstm_6

Pairs trading using the Kalman Filter and a LSTM forecast model

fx_lstm_7

Machine learning project using LSTM network to predict the direction of exchange rate for a currency pair.

fxcm-trading

fxcm_api

Extracting history OHLC data from fxcm server

fxcmapi

fxcmconnectapi

Getting Price History

fxcmforexpython

FXCMForexPython

fxcmpy

FXCM python wrapper

fxcmpy-latest

Created from the python-socketio library

fxcmpy_automated_trading

Uses the FXCM python API to execute trades without user intervention

fxcmrest-py

fxcmrestapi

Open-source code I wrote using FXCM's REST API

fxcmtickdata

FXCM Tick data that will be updated weekly.

fxcmtimeseriesdata

FXCM historical Time Series or Candle Data

fxcollect

Automate the simultaneous collection of historical and live financial data from FXCM

fxcpy

fxcpy is an open-soured python implementation of the Forexconnect API SDK offered by FXCM

fxforward

Counterparty credit risk calculator

fxpy

A python library for modeling and analyzing fixed income securities

ga-dashboard-demo

A demo on how to build your own Google Analytics dashboard with R, Shiny and MySQL

ga-for-portfolio-management

This program is a python implementation of a Genetic Algorithm. It returns a fair distribution of funds over certain avenues of investment according to the parameters input by the user.

gallery

A gallery of Python scripts and reports

gar

gaussian-copula-factor-model

"Bayesian Gaussian Copula Factor Models for Mixed Data" by Jared S. Murray, David B. Dunson, Lawrence Carin, Joseph E. Lucas. This is a read-only mirror of the CRAN R package repository.

gdp

Economic indicators of country

gdpanalysis

Problem Description - I Background - NITI Aayog NITI Aayog (National Institution for Transforming India) is a policy think tank of the Government of India which provides strategic inputs to the central and the state governments to achieve various development goals. In the past, NITI Aayog has played an important role in initiatives such as Digital India, Atal Innovation Mission, various agricultural reforms etc. and have designed various policies in education, skill development, water management, healthcare, etc. NITI Aayog was established to replace the Planning Commission of India which used to follow a top-down model for policy making, i.e. it typically designed policies at the central level (such as the various 5-year plans). On the other hand, NITI Aayog designs policies specific to the different states or segments of the economy. Finance Minister Arun Jaitley made the following observation on the necessity of creating NITI Aayog, "The 65-year-old Planning Commission had become a redundant organisation. It was relevant in a command economy structure, but not any longer. India is a diversified country and its states are in various phases of economic development along with their own strengths and weaknesses. In this context, a ‘one size fits all’ approach to economic planning is obsolete..." Project Brief You are working as the chief data scientist at NITI Aayog, reporting to the CEO. The CEO has initiated a project wherein the NITI Aayog will provide top-level recommendations to the Chief Ministers (CMs) of various states which will help them prioritise areas of development for their respective states. Since different states are in different phases of development, the recommendations should be specific to states as well. The overall goal of this project is to help the CMs focus on areas which will foster economic development for their respective states. Since the most common measure of economic development is the GDP, you will analyse the GDP of the various states of India and suggest ways to improve it. Understanding GDP Gross domestic product (GDP) at current prices is the GDP at the market value of goods and services produced in a country during a year. In other words, GDP measures the 'monetary value of final goods and services produced by a country/state in a given period of time'. GDP can be broadly divided into goods and services produced by three sectors: the primary sector (agriculture), the secondary sector (industry), and the tertiary sector (services). It is also known as nominal GDP. More technically, (real) GDP takes into account the price change that may have occurred due to inflation. This means that the real GDP is nominal GDP adjusted for inflation. We will use the nominal GDP for this exercise. Also, we will consider the financial year 2015-16 as the base year since most of the data required for this exercise is available for the above period. Per Capita GDP and Income Total GDP divided by the population gives the per capita GDP (which roughly measures the average value of goods and services produced per person). The per capita income is closely related to per capita GDP (though they are not the same). In general, the per capita income increases when per capita GDP increases and vice-versa. For instance, in the financial year 2015-16, the per capita income of India was Rs 93,293, whereas the per capita GDP of India was $1717 which roughly translates to Rs 1,11,605. India ranks