Jupyter Signal Generation & Report
Budget: $30 – $250 USD
I need assistance with signal generation and report writing for a trading strategy on the SPY ETF, implemented through Jupyter.
Specifically, I'm looking for:
Exchange-traded Funds (ETFs) provide access to a basket of assets without having to buy all the
components individually. The fund provider, who owns the underlying assets, designs a fund to track
their performance and then sells shares in that fund to investors.
Some advantages of ETFs are lower transactions costs, instant diversification, liquidity, tax efficiency,
sectoral investing, the ability to purchase small amounts, and the availability of a wide variety of
alternative, and even exotic, investments.
This assignment requires you to construct a signal-based trading strategy for one particular exchangetraded fund, SPY; an ETF which tracks the S&P500 Index. To undertake this analysis, you will be
required to build a multiple linear regression model, using time-series data from a range of global
indices, to predict daily price changes in SPY. The model must be built using Python programming in
the Jupyter Notebook environment. You should interpret, present, and report your findings.
Ideal skills and experience for this project include:
- Strong background in finance and trading strategies
- Proficiency in Jupyter for signal generation
- Excellent analytical and report writing skills in the finance domain
- Previous experience with moving average crossover signals in trading strategies would be a plus.
Specifically, I'm looking for:
Exchange-traded Funds (ETFs) provide access to a basket of assets without having to buy all the
components individually. The fund provider, who owns the underlying assets, designs a fund to track
their performance and then sells shares in that fund to investors.
Some advantages of ETFs are lower transactions costs, instant diversification, liquidity, tax efficiency,
sectoral investing, the ability to purchase small amounts, and the availability of a wide variety of
alternative, and even exotic, investments.
This assignment requires you to construct a signal-based trading strategy for one particular exchangetraded fund, SPY; an ETF which tracks the S&P500 Index. To undertake this analysis, you will be
required to build a multiple linear regression model, using time-series data from a range of global
indices, to predict daily price changes in SPY. The model must be built using Python programming in
the Jupyter Notebook environment. You should interpret, present, and report your findings.
Ideal skills and experience for this project include:
- Strong background in finance and trading strategies
- Proficiency in Jupyter for signal generation
- Excellent analytical and report writing skills in the finance domain
- Previous experience with moving average crossover signals in trading strategies would be a plus.