1.8) Research Study 1.0: Financial Data Representations | Quantitative Alpha R&D for Traders

In this video section we will talk about what is a representation in quantitative analysis, the intrinsic fundamentals and properties of candle representations and the optimal approach to use and understand information arrival rate in electronic financial markets.
Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees:
www.darwinex.com/?...
We will dissect why it is crucially important to take care of how we interpret time-series financial data and how we aggregate it to form the so called candles or bars.
All research code referenced in these tutorials is available via the following GitHub repo:
github.com/darwinex/quant-res...
-----------------------
IMPORTANT REQUEST: Please please please.. if you find this content useful, please do consider liking and sharing it on KZread, Twitter, Facebook, LinkedIn and whatever other social networks you have circles in.
Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees:
www.darwinex.com/?...
Darwinex relies almost exclusively on organic growth, primarily through recommendation via informative content.
KZread’s algorithms measure the quality of Darwinex content on the basis of:
- Reach
- Engagement
- and several other related variables
With seemingly small actions such as:
- Clicking the Like button
- Clicking the Subscribe button
- Clicking the Share button (on KZread) and distributing our content
- etc
… YOU inform KZread’s algorithms of your sentiment towards Darwinex, thereby directly helping Darwinex MASSIVELY in achieving organic growth.
Thank you very much for your kind consideration!
-----------------------
Risk disclosure:
www.darwinex.com/legal/risk-d...
** Fancy joining a vibrant community of algorithmic traders, quants and data scientists focused on financial hacking? Join the Darwinex Collective Slack Workspace:
join.slack.com/t/darwinex-col...
#FinancialData, #QuantTrading, #MachineLearning, #ArtificialIntelligence, #QuantitativeFinance

Пікірлер: 4

  • @TradeLikeAMachine
    @TradeLikeAMachine4 жыл бұрын

    Interesting concept of not fixing bars - looking forward to seeing future episodes to see how this is done....

  • @marticastany
    @marticastany4 жыл бұрын

    That's a very interesting topic. I understand from your previous video series that the end goal here is being able to determine on which "regime" we are in (or at least which is the degree of volatility of the current situation). It could be interesting to compute the slope of the std dev curve so maybe it can have some predicting power about future volatility conditions. We don't know the function that drives that vol curve, but with the return distributions and a discrete rolling first derivative decomposing the curve with straight lines, a volatility predicion model can be constructed. Keep up with this series as they are super interesting. Regards!

  • @TradeLikeAMachine
    @TradeLikeAMachine4 жыл бұрын

    This is getting really interesting now. Great to see scientific rigor being put into action on concepts I have 'played' with in the past. This is getting me excited again in terms of the concept of dynamic parameters

  • @ihebbibani7122
    @ihebbibani71222 жыл бұрын

    So if I do understand.... To know that our distribution has changed we look at the volatility changes which means here that the Market is Dynamic . If The rate of the volatility is also changing , it means that the Market Dynamic is DYNAMIC. Otherwise , the Market Dynamic is DETERMINISTIC Does it join your explanations on the "1.3) Optimal Approach to Modeling Markets | Quantitative Alpha R&D for Traders video ?" But of course , the percentage change (which will give us the rate change) of the volatility will be changing thus the market is dynamically dynamic. Are my explanations correct ? Thank you