Sub-Thread 2
Finding “Edges”
Adopting Alpha Research Methods
Finding “Edges”
Adopting Alpha Research Methods
📌 Finding “Edges”
1)
Type 1: Understanding market structure and market incentives, and the corresponding flows
Type 2: Finding statistical anomalies within price/non-price data
Type 3: Hybrid approach.
Let’s seek to understand this further.
1)
Type 1: Understanding market structure and market incentives, and the corresponding flows
Type 2: Finding statistical anomalies within price/non-price data
Type 3: Hybrid approach.
Let’s seek to understand this further.
2)
Type 1: There are many reasons for actionable price flows, such as price insensitive liquidation, factor premia et cetera. For an example, a previous thread
Type 1: There are many reasons for actionable price flows, such as price insensitive liquidation, factor premia et cetera. For an example, a previous thread
https://twitter.com/HangukQuant/status/1442970057827491840?s=20
3)
These have intuitive explanations. Assume an option writing strategy, specifically short puts. This should be a positive expected return strategy, since you are acting as the “fire insurer” for a market burn down. You take on the burden of risk and collect “risk” premiums.
These have intuitive explanations. Assume an option writing strategy, specifically short puts. This should be a positive expected return strategy, since you are acting as the “fire insurer” for a market burn down. You take on the burden of risk and collect “risk” premiums.
4)
Type 2: Finding statistical anomalies. Techniques in this domain are but many, including statistical and artificial intelligence methods. However, these are often abused by engineers, data scientists and “domain experts” who understand little about the markets themselves.
Type 2: Finding statistical anomalies. Techniques in this domain are but many, including statistical and artificial intelligence methods. However, these are often abused by engineers, data scientists and “domain experts” who understand little about the markets themselves.
5) Their techniques, while ingenious, are often little suit for practical, financial trading. In GENERAL, increasingly inductive (2, 3, 1) approaches are likely to be more persistent, and have the added benefit of interpretability/conviction in its inference.
6) However, these approaches tend to be symbiotic, and a hybrid approach reflexively improves the process. That is, understanding factors driving the market guides the search for statistical anomalies, while statistical anomalies hints at relationships of underlying variables.
📌 Adopting Alpha Research Methods
7)
Type 1: Unfortunately, there are no shortcuts in understanding driving market factors. Reading macroeconomic analysis, understanding market players, seasonal effects, fundamental analysis are all helpful, but there is no one-size fit all.
7)
Type 1: Unfortunately, there are no shortcuts in understanding driving market factors. Reading macroeconomic analysis, understanding market players, seasonal effects, fundamental analysis are all helpful, but there is no one-size fit all.
8) A good approach is to read more literature, as well as reliable analysts who opine on financial events. In addition, financial literature and mathematics are also important.
9) It is reasonable that one trading options should understand the Greeks, as well as the assumptions behind the pricing model (BSM).
10) An accessible class of alpha that does not require understanding of “complex” macroeconomics or financial mathematics are Risk Premias, and a booked recommended by @therobotjames:
11) Book: Expected Returns: An Investor's Guide to Harvesting Market Rewards
These are often accessible to retail traders to harvest and relatively easy to intuit.
These are often accessible to retail traders to harvest and relatively easy to intuit.
12)
Type 2: 1)Yet again unfortunately, there are no shortcuts in understanding the theoretical underpinnings of statistical methods.
Type 2: 1)Yet again unfortunately, there are no shortcuts in understanding the theoretical underpinnings of statistical methods.
13) Standard AI algorithms often fail to find persistent statistical anomalies in a problem setting where the hypothesis space is both large, dynamic and probabilistic with low signal to noise ratio.
My thread on application of AI in markets:
My thread on application of AI in markets:
https://twitter.com/hangukquant/status/1442155794334502912?s=21
14) Alphas exist as sparse, non-persistent, decaying subspaces within an infinite-dimensional Hilbert space. Even worse, they have high tendency towards false positives.
15) Applying standard AI techniques almost invariably leads to absurdly high Sharpe ratios from alphas that overfit complex, non-linear unstable relationships between said variables, but are practically useless in predictive power due to the properties of the problem setting.
16) However, such statistical learning methods can become useful when “hacked”, often relying on heuristics designed to cleverly overcome said complexities that both reduce search spaces and counter false positives.
17) Design of such “algorithm-hacking methods” requires understanding the mathematics behind the algorithms, but also knowledge of a wide range of algorithms to be used under different settings.
18) The “hacking” part is then adjusted to the knowledge of the trading domain, which can only be achieved by yet again understanding (Type 1).
19) MAIN THREAD
https://twitter.com/hangukquant/status/1445464869587746818
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