Hi, and once again, welcome to this course on algorithmic trading.
Now let me briefly explain what we will do in this course and let me give you a short course overview.
So there is actually no single definition of algorithmic trading and the different people, I mean,
different things, but all use cases and applications have two things in common.
So there are two major aspects of algorithmic trading.
And first of all, trades follow a clear strategy or plan.
And either we have pre-programmed rules for algorithms or we have a learning algorithms like machine
learning.
And second, the trades that automated and executed by computers, not humans.
And this enables high frequency trading and trading based on complex strategies that are too complex
or too fast for the human brain.
Next, let's have a look at some selected use cases and applications of algorithmic trading and recently
algo trading was used for trade, execution and transaction cost reduction.
So in simple words, let's assume that a bank wants to sell one million shares of Apple, then it might
not be the best idea to sell all shares at once.
So the bank should break down a large order and two small orders to be placed over time to optimize
the price and the transaction costs.
Second, we have portfolio rebalancing and algorithmic trading can be used to rebalance portfolio constituents
to the target where it's.
Now, let's come to number three and one of the newer applications are robo investing or robo advising
and asset allocation, portfolio optimization, management and rebalancing is done with minimal human
intervention.
So this is typically for long term investments.
And finally, we have market timing, alpha generating strategies, and that's what this course is all
about.
So we timed trades and the take long and short positions and instruments like currencies to benefit
from mispricing or market inefficiencies.
And this should of independent from the general market trends.
So it works in both and also in bear markets.
And actually we would use the technical indicators like moving averages and other techniques like machine
learning.
So that's the plan for this cause.
Algorithmic day trading.
So this causes organized and five parts and then part one, you will learn the basics of day trading
from A to Z, then in part to be able to cover some python workflows that we will need for the next
parts, like managing and handling financial data was Panas and introduction to object oriented programming.
Then part three is the heart of this cause defining strategy is based on technical indicators and also
machine learning based strategies, and you will learn how to back tests and fovea tests the strategies
and identify those strategies that are profitable.
So the main focus here is on trading costs because the strategies must be profitable after trading costs.
Finally, we have part four where you will learn how to create a trading bots to implement your strategies
and to automate your trades.
And then we have another part, Part five and Part five provides some really helpful expert tips and
tricks and also has some comprehensive case studies.
And finally, in the appendix, you can find a python crash course to refresh your skills on a basic
python coding and also the appendixes for those students who haven't worked with Python before.
Finally, to be fair and transparent to everyone.
For some sections and lectures, you need a brokerage account either on the end or seem to get the full
and the best learning experience.
So it's the part on day trading and also the part on implementation and automation.
However, in some countries, the CFT or Forex trading is prohibited or the two brokers are not available
in those countries.
And the Fox sample raffia Japan, Korea, Russia and Turkey.
But I do think that this course provides a lot of value and benefits even if you don't have a broker
account.
So but so please keep in mind that the two brokers are not available in all countries.
All right, this was a brief overview.
Thanks for watching and see you in the next Lexus by.