BApig

@bapigindicator

Trading is a science not art. - for exceptional plebs - for knowledge hungry mfers

Joined September 2021

157 Following

96 Followers

118 Posts

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BApig @bapigindicator

6 months ago

The Illusion of TA vs. The Harsh Reality of Quantitative Trading Let’s explore a critical aspect of trading: the disparity between the illusions created by Technical Analysis (TA) and the often harsh reality of quantitative trading. It’s time to break down most common illusions and examine why these two approaches yield such different outcomes. The Allure of TA TA tools like RSI, MACD, moving averages, chart patterns and suport/resistance trendlines are popular for their simplicity. Traders can easily spot patterns, which creates the illusion that success is just about recognising and acting on these signals. This approach makes trading feel intuitive and straightforward, leading to a sense of control over market movements. False Sense of Control The allure of TA can breed overconfidence. The belief that one can "time" entries and exits based on historical patterns can be misleading. This false sense of control often leads traders to believe that mastering TA is the key to consistent profits. Ignoring Market Complexity However, what TA often overlooks is the inherent complexity of financial markets. Price movements are influenced by a great sum of factors, including economic data, geopolitical events, and market sentiment. By focusing solely on price patterns, TA reduces this complexity and creates an incomplete picture of market dynamics. Market Inefficiencies Moreover, TA fails to identify true market inefficiencies/price discrepancies that could be exploited for profit. These inefficiencies often arise from irrational behaviour among market participants. Unfortunately, TA indicators lack rigorous statistical validation, they’re more akin to educated guesses than thorough analyses. Chasing Illusions Traders using TA often fall into the pattern-chasing trap. What one trader interprets as a bullish signal, another may see as bearish. This subjectivity can lead to inconsistent decision-making and, ultimately, poor trading outcomes. Traders may also succumb to confirmation bias, only recognising patterns that reinforce their beliefs while ignoring contrary evidence. Quantitative Indicators: Rigorous but Rare In contrast, quantitative indicators aim to incorporate statistical rigour into trading. These methods also analyse historical data and employ sophisticated models and statistical tests to identify true market edges. Truly rigorous quantitative tools are very rare in the trading space and that goes double for the retail space. Understanding True Inefficiencies True market inefficiencies, like persistent mispricings due to behavioural biases or market constraints, can occasionally be quantified and exploited through quantitative tools. Yet, these inefficiencies are typically rare and fleeting, often eroding quickly as more participants leverage similar strategies. Exposing Limitations Quantitative trading reveals the complexity and randomness of financial markets. Unlike the perceived certainty of TA, the reality of quantitative models often uncovers the limitations of previously identified edges. Many backtests of strategies lack statistical rigour and validity. For instance, a strategy that appears profitable in backtesting might only succeed because it fits historical data too closely. This is known as overfitting. Example: Imagine a trading strategy that has you buying based solely on a moving average crossover in a trending market. The backtest shows impressive profits over the past decade, but it was designed to account for that specific market condition. When market conditions shift or enter a sideways range, the strategy may perform poorly, exposing its limited applicability. Without proper statistical validation methods such as cross-validation or out-of-sample testing, traders may be misled into believing the strategy is robust when it's only a product of the specific dataset used. Usually, when TA tools are combined with a quantitative approach, the illusion quickly falls apart. Quantitative tools are often not the most attractive to traders, as they paint the market picture in the most realistic way. And the reality of the markets is often disappointing for those who see it as an escape or an easy way to financial freedom! The key point you should take away from this thread is not necessarily that TA indicators don't tell you anything useful about the market. Some might...sometimes... rarely...almost never😁. The key point is that they do not offer or help you identify a market edge in the form of an inefficiency/mispricing.

bapigindicator's tweet photo. The Illusion of TA vs. The Harsh Reality of Quantitative Trading

Let’s explore a critical aspect of trading: the disparity between the illusions created by Technical Analysis (TA) and the often harsh reality of quantitative trading. It’s time to break down most common illusions and examine why these two approaches yield such different outcomes.

The Allure of TA

TA tools like RSI, MACD, moving averages, chart patterns and suport/resistance trendlines are popular for their simplicity. Traders can easily spot patterns, which creates the illusion that success is just about recognising and acting on these signals. This approach makes trading feel intuitive and straightforward, leading to a sense of control over market movements.

False Sense of Control

The allure of TA can breed overconfidence. The belief that one can "time" entries and exits based on historical patterns can be misleading. This false sense of control often leads traders to believe that mastering TA is the key to consistent profits.

Ignoring Market Complexity

However, what TA often overlooks is the inherent complexity of financial markets. Price movements are influenced by a great sum of factors, including economic data, geopolitical events, and market sentiment. By focusing solely on price patterns, TA reduces this complexity and creates an incomplete picture of market dynamics.

Market Inefficiencies

Moreover, TA fails to identify true market inefficiencies/price discrepancies that could be exploited for profit. These inefficiencies often arise from irrational behaviour among market participants. Unfortunately, TA indicators lack rigorous statistical validation, they’re more akin to educated guesses than thorough analyses.

Chasing Illusions

Traders using TA often fall into the pattern-chasing trap. What one trader interprets as a bullish signal, another may see as bearish. This subjectivity can lead to inconsistent decision-making and, ultimately, poor trading outcomes. Traders may also succumb to confirmation bias, only recognising patterns that reinforce their beliefs while ignoring contrary evidence.

Quantitative Indicators: Rigorous but Rare

In contrast, quantitative indicators aim to incorporate statistical rigour into trading. These methods also analyse historical data and employ sophisticated models and statistical tests to identify true market edges. Truly rigorous quantitative tools are very rare in the trading space and that goes double for the retail space.

Understanding True Inefficiencies

True market inefficiencies, like persistent mispricings due to behavioural biases or market constraints, can occasionally be quantified and exploited through quantitative tools. Yet, these inefficiencies are typically rare and fleeting, often eroding quickly as more participants leverage similar strategies.

Exposing Limitations

Quantitative trading reveals the complexity and randomness of financial markets. Unlike the perceived certainty of TA, the reality of quantitative models often uncovers the limitations of previously identified edges. Many backtests of strategies lack statistical rigour and validity. For instance, a strategy that appears profitable in backtesting might only succeed because it fits historical data too closely. This is known as overfitting.

Example: Imagine a trading strategy that has you buying based solely on a moving average crossover in a trending market. The backtest shows impressive profits over the past decade, but it was designed to account for that specific market condition. When market conditions shift or enter a sideways range, the strategy may perform poorly, exposing its limited applicability. Without proper statistical validation methods such as cross-validation or out-of-sample testing, traders may be misled into believing the strategy is robust when it's only a product of the specific dataset used.

Usually, when TA tools are combined with a quantitative approach, the illusion quickly falls apart. Quantitative tools are often not the most attractive to traders, as they paint the market picture in the most realistic way. And the reality of the markets is often disappointing for those who see it as an escape or an easy way to financial freedom!

The key point you should take away from this thread is not necessarily that TA indicators don't tell you anything useful about the market. Some might...sometimes... rarely...almost never😁. The key point is that they do not offer or help you identify a market edge in the form of an inefficiency/mispricing.

173

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Michael Hunt

@Mike_Hunt_Sr

over 2 years ago

bapigindicator retweeted

Bloomberg

@business

over 2 years ago

The Fed should stop trying to micromanage the economy and use the free time to practice golf: JPMorgan Asset Management's David Kelly https://t.co/uf1qseQ6bH

33K

bapigindicator retweeted

Sir Of Finance @SirOfFinance

over 2 years ago

🚨Bloomberg seems to have introduced Option Implied Probabilities in BECO. Very handy tool, still not exactly what most people need, nonetheless still handy. First chart is current pricing. Second chart is the historic change in the 10th decile.