How to find an edge ?

Price hurts. Size kills.

 

The spread is an edge. That is why cfds run there entire business on just capturing the spread.

Casinos in sports betting want even action on both sides because there edge is the "spread" they bake into the lines.

Now the retail trader doesn't have that.

But what they do have is the ability to weave in and out of the market.

The large players have to deal with size and getting front run constantly in the books.

The retail traders advantage is the abilities to get in and out of positions with no impact on the order flow of things.

 

There isn't a single "most profitable" trading algorithm, as profitability depends on various factors, including market conditions, the type of assets being traded, and the specific strategies employed. However, here are some popular and historically profitable trading algorithms:

1. Market Making Algorithms

1. Description: These algorithms place buy and sell orders at slightly higher and lower prices, profiting from the bid-ask spread. Market makers provide liquidity to the market.
2. Profitability: High in liquid markets, especially for high-frequency trading (HFT) firms.
3. Risks: Vulnerable to market volatility and adverse selection.

2. Trend Following Algorithms

1. Description: These algorithms identify and exploit trends in the market, buying when prices are rising and selling when they are falling.
2. Profitability: Can be highly profitable in trending markets.
3. Risks: Suffer in sideways or choppy markets where trends are not clear.

3. Mean Reversion Algorithms

1. Description: These algorithms assume that prices will revert to their mean or average level. They buy when prices are below the mean and sell when prices are above.
2. Profitability: Effective in range-bound markets.
3. Risks: Can be unprofitable during strong trends away from the mean.

4. Statistical Arbitrage (StatArb)

1. Description: This strategy involves exploiting pricing inefficiencies between related securities, often using complex mathematical models to identify opportunities.
2. Profitability: High potential in market-neutral strategies, often used by hedge funds.
3. Risks: Requires significant computational power and sophisticated models. Market conditions can shift, leading to unexpected losses.

5. Machine Learning and AI-Based Algorithms

1. Description: These algorithms use machine learning models to predict market movements based on historical data, news, social media sentiment, and other data sources.
2. Profitability: High potential due to the ability to adapt and learn from new data.
3. Risks: Can be complex and difficult to interpret. Overfitting and model drift are common issues.

6. High-Frequency Trading (HFT) Algorithms

1. Description: These algorithms execute a large number of orders at extremely high speeds, often holding positions for milliseconds or seconds.
2. Profitability: Can be highly profitable due to the speed advantage, particularly in markets with high liquidity.
3. Risks: Requires significant investment in technology and infrastructure. Regulatory risks are also a concern due to increased scrutiny.

7. Sentiment Analysis-Based Algorithms

1. Description: These algorithms analyze news, social media, and other textual data to gauge market sentiment and make trading decisions.
2. Profitability: Effective when there is a clear relationship between sentiment and market movements.
3. Risks: Can be vulnerable to false signals and requires constant updating of the sentiment model.

Key Considerations:

1. Market Conditions: The effectiveness of an algorithm can vary depending on whether the market is trending, volatile, or stable.
2. Asset Class: Different algorithms work better for different asset classes (e.g., stocks, forex, commodities).
3. Technology: The speed and accuracy of the algorithm can significantly impact profitability, especially in high-frequency trading.
4. Risk Management: Even the most profitable algorithms can result in significant losses without proper risk management.

The most profitable trading algorithm is often one that is well-tuned to the specific market and trading environment in which it is used. Continuous monitoring, adjustment, and risk management are essential to maintain profitability.

 

8. BUCKET SHOPS

These businesses provide gambling services to the public, offering a real-time display of market prices of various assets while allowing them to buy or sell a facsimile of the asset from the broker, rather than a real counterparty at the exchange, and settle up the win or loss after some set time. The odds favor the broker, in most cases, since they usually know in advance who is a consistent loser! The disadvantage of this business model is the broker has no direct control over the size, frequency, and asset name that will be tracked as they take the other side. Elite traders, however, can emulate this model without the limitations if they have access to account balance trajectory information on several losing traderz

 

Chat gpt is awesome. I use it for formulas in google sheets all the time.

 

Why would you need an edge? Trading is the easiest occupation in the world. It literally requires no education or skill to make money.

 

Haven't used it myself yet. My friend says I'm getting behind the curve if I'm not starting soon.

He uses it for a ton of stuff. Mostly work related (not finance).

 

Curious about your prompt chain if you are willing to share. Looking to see the difference in models/userhistory from the same prompt.

 

The reasoning skills are improving rapidly, to wit; AlphaGeometry:
https://www.nature.com/articles/s41586-023-06747-5

A YTuber going through AlphaGeometry's reasoning process:

 

Sorry but I don’t have the exact history anymore.

I did mention advantage gambling then I asked how to find an edge.