Day trading strategy

Day trading strategy

What are the risks of online trading?

There is risk of loss associated with investing in securities regardless of the method used. New investors need to understand the principles of investing, their own risk tolerance, and their investment goals before venturing into the market. In addition, online investors may want to consider these other risks. High Internet traffic may affect online investors' ability to access their account or transmit their orders. Online investors should be skeptical of stock advice and tips provided in chat rooms or bulletin boards. Investors should do their own research before acting on these tips. Also, for some online investors, there is a temptation to "overtrade" by trading too frequently or impulsively without considering their investment goals or risk tolerance. Overtrading can effect investment performance, raise trading costs, and complicate your tax situation.

What does it mean to 'trade on margin'?

If a customer chooses to borrow funds from a firm, the customer will open a margin account with that firm. The portion of the purchase price that the customer must deposit is called margin and is the customer's initial equity in the account. The loan from the firm is secured by the securities that are purchased by the customer. Customers generally use margin to leverage their investments and increase their purchasing power. At the same time, customers who trade securities on margin incur the potential for higher losses; therefore, customers should make sure they clearly understand this concept before opening a margin account and entering the investing arena. For more information, including a specific example, click here.

Where can I get more information?

We have published guidance and other information for members and investors on the issue of online investing, as well as information about what to look out for when investing in general.

General Investor Information

Margin Accounts

Email  /  CV  /  LinkedIn  /  GitHub

Krishna Face
Publications

Accepted

VaPar Synth - A Variational Parametric Model for Audio Synthesis
Krishna Subramani, Preeti Rao Alexandre D'Hooge
ICASSP 2020
Paper / Code / Audio Examples / DOI / BibTeX

We present VaPar Synth - a Variational Parametric Synthesizer for instrument note synthesis, which utilizes a conditional variational autoencoder trained on a source-filter inspired parametric representation.

We will be presenting our work virtually at ICASSP 2020! (Presentation Video)

Energy-Weighted Multi-Band Novelty Functions for Onset Detection in Piano Music
Krishna Subramani, Srivatsan Sridhar, Rohit M A, Preeti Rao
National Conference on Communications 2018
Paper / DOI / BibTeX

Propose the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold to improve detection of soft onsets in the vicinity of loud notes. Also propose a grouping algorithm to reduce the detection of spurious onsets.

We presented our work at the conference as an oral presentation

Generative Audio Synthesis with a Parametric Model
Krishna Subramani, Alexandre D'Hooge, Preeti Rao
ISMIR 2019 Late Breaking/Demo
Abstract / Audio Examples / BibTeX

Propose a parametric representation for audio corresponding more directly to its musical attributes such as pitch, dynamics and timbre. For more control over generation, we also propose the use of a conditional variational autoencoder which conditions the timbre on pitch.

We presented our work as a poster in the ISMIR 2019 Late Breaking/Demo session

Submitted

Learning Complex Representations from Spatial Phase Statistics of Natural Scenes
HaDi Maboudi, Krishna Subramani, Hamid Soltanian-Zadeh, Shun-ichi Amari, Hideaki Shimazaki
Submitted to Neural Networks, Under Review
Preprint / bioRxiv / BibTeX

We introduce a generative model for phase in visual systems, and propose a complex domain based maximum likelihood estimation procedure for parameter estimation. We derive analytical gradient expressions for maximum likelihood estimation using Wirtinger Calculus (detailed in our supplementary material)

I presented the initial part of this work as an oral presentation at Honda Research Institute

Research Experience
Variational Parametric Models for Audio Synthesis
Report / Repository / Review of Generative Models for Audio Synthesis

(Ongoing)My Master's thesis work on generative parametric models for audio synthesis.

I presented the work done by me so far as an oral presentation for the first stage defense of my Master's thesis

Compression using Graph Signal Processing
Presentation / Report / Notebook Demonstration of the Code

Inspired by the authors work on compressing distributed data by modeling it as graphs, we decided to try out and extend the idea to a new dataset by performing outlier removal, data imputation and clipping, and were able to successfully compress it at a significantly faster rate(~8x speedup) than conventional techniques.

https://www.sfu.ca/~truax/gran.html
Data Sonification using Granular Synthesis
Report / Code Repository

Implemented a real-time granular synthesizer on Pure Data. Also ideated a scheme where the parameters of the synth could be controlled externally with data, thus 'sonifying' the data!

Kuramoto Model and Oscillatory Networks
Image Recall

Implemented an image retrieval(memory recall) task using the Kuramoto Model. Extended this to try out graph coloring using Oscillatory Networks.


The Master Yoda to us Padawans.

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