Hey everyone, I am a former Wall Street trader and quant researcher. When I was preparing for my own interviews, I have noticed the lack of accurate information and so I will be providing my own perspectives. One common pattern I see is people building their own algorithm by blindly fitting statistical methods such as moving averages onto data.
I have written this article initially on Medium, but I have copy pasted it entirely below for you to read to keep it in the spirit of the sub rules.
What it was like trading on Wall Street
Right out of college, I began my trading career at an electronic hedge fund on Wall Street. Several friends pitched trading to me as being a more disciplined version of r/wallstreetbets that actually made money. After flopping several initial interviews, I was fortunate to land a job at a top-tier firm of the likes of Jane Street, SIG, Optiver and IMC.
On my first day, I was instantly hooked.
My primary role there was to be a market maker. To explain this, imagine that you are a merchant. Suppose you wanted to purchase a commodity such as an apple. You would need to locate an apple seller and agree on a fair price. Market makers are the middle-men that cuts out this interaction by being always willing to buy or sell at a given price.
In finance lingo, this is called providing liquidity to financial exchanges. At any given moment, you should be confident to liquidate your position for cash. To give a sense of scale, tens of trillions in dollars are processed through these firms every year.
My time trading has been one of the most transformative periods of my life. It not only taught me a lot of technical knowledge, but it also moulded me to be a self-starter, independent thinker, and hard worker. I strongly recommend anyone that loves problem solving to give trading a shot. You do not need a mathematics or finance background to get in.
The trading culture is analogous to professional sports. It is a zero sum game where there is a clear defined winner and loser — you either make or lose money. This means that both your compensation and job security is highly dependent on your performance. For those that are curious, the rough distribution of a trader’s compensation based on performance is a tenth of the annual NBA salary.
There is a mystique about trading in popular media due to the abstraction of complicated quantitative models. I will shed light on some of the fundamental principles rooted in all trading strategies, and how they might apply to you.
One way traders make money is through an arbitrage or a risk free trade. Suppose you could buy an apple from Sam for $1, and then sell an apple to Megan at $3. A rational person would orchestrate both legs of these trades to gain $2 risk free.
Arbitrages are not only found in financial markets. The popular e-commerce strategy of drop-shipping is a form of arbitrage. Suppose you find a tripod selling on AliExpress at $10. You could list the same tripod on Amazon for $20. If someone buys from you, then you could simply purchase the tripod off AliExpress and take home a neat $10 profit.
The same could be applied to garage sales. If you find a baseball card for $2 that has a last sold price on EBay for $100, you have the potential to make $98. Of course this is not a perfect arbitrage as you face the risk of finding a buyer, but the upside makes this worthwhile.
Positive expected value bets
Another way traders make money is similar to the way a casino stacks the odds in their favour. Imagine you flip a fair coin. If it lands on heads you win $3, and if it lands on tails you lose $1. If you flip the coin only once, you may be unlucky and lose the dollar. However in the long run, you are expected to make a positive profit of $1 per coin flip. This is referred to as a positive expected value bet. Over the span of millions of transactions, you are almost guaranteed to make a profit.
This exact principle is why you should never gamble in casino games such as roulette. These games are all negative expected value bets, which guarantees you to lose money over the long run. Of course there are exceptions to this, such as poker or card counting in black jack.
The next time you walk into a casino, make a mental note to observe the ways it is designed to keep you there for as long as possible. Note the lack of windows and the maze like configurations. Even the free drinks and the cheap accommodation are all a farce to keep you there.
Relative pricing is a great strategy to use when there are two products that have clear causal relationships. Let us consider an apple and a carton of apple juice. Suppose there have a causal relationship where the carton is always $9 more expensive than the apple. The apple and the carton is currently trading at $1 and $10 respectively.
If the price of the apple goes up to $2, the price is not immediately reflected on the carton. There will always be a time lag. It is also important to note that there is no way we can determine if the apple is trading at fair value or if its overpriced. So how do we take advantage of this situation?
If we buy the carton for $10 and sell the apple for $2, we have essentially bought the ‘spread’ for $8. The spread is fairly valued at $9 due to the causal relationship, meaning we have made $1. The reason high frequency trading firms focus so much on latency in the nanoseconds is to be the first to scoop up these relative mispricing.
This is the backbone for delta one strategies. Common pairs that are traded against each other includes ETFs and their inverse counterpart, a particular stock against an ETF that contains the stock, or synthetic option structures.
Correlations are mutual connections between two things. When they trend in the same direction they are said to have a positive correlation, and the vice versa is true for negative correlations. A popular example of positive correlation is the number of shark attacks with the number of ice-cream sales. It is important to note that shark attacks do not cause ice-cream sales.
Often times there are no intuitive reason for certain correlations, but they still work. The legendary Renaissance Technologies sifted through petabytes of historical data to find profitable signals. For instance, good morning weather in a city tended to predict an upward movement in its stock exchange. One could theoretically buy stock on the opening and sell at noon to make a profit.
One important piece of advice is to disregard any retail trader selling a course to you, claiming that they have a system. These are all scams. At best, these are bottom of the mill signals that are hardly profitable after transaction costs. It is also unlikely that you have the system latency, trading experience or research capabilities to do this on your own. It is possible, but very difficult.
Another common strategy traders rely on is mean reversion trends. In the options world the primary focus is purchasing volatility when it is cheap compared to historical values, and vice versa. Buying options is essentially synonymous with buying volatility. Of course, it is not as simple as this so don’t go punting your savings on Robinhood using this strategy.
For most people, the most applicable mean reversion trend is interest rates. These tend to fluctuate up and down depending on if the central banks want to stimulate saving or spending. As global interest rates are next to zero or negative, it may be a good idea to lock in this low rate for your mortgages. Again, consult with a financial advisor before you do anything.
Submitted October 04, 2020 at 03:41AM by chriswugan