Embracing the Basics: The Value of Starting from the Fundamentals in Quant Finance
quantymacro @ twitter
orig
I find value in embracing the basics; they’re the foundation for deeper understanding. Critics labeling my insights as "too basic" miss the point—it's a signal of growth potential. Sticking to fundamentals at this stage sets me up to tackle more complex topics down the line. If I'm already advanced and still presenting basic concepts, that would indeed be concerning. Focusing on low-hanging fruit allows me to steadily build my knowledge and skills without feeling overwhelmed. It's a strategic approach to learning and mastering more intricate ideas as I progress.
@quant_feed
quantymacro @ twitter
orig
I find value in embracing the basics; they’re the foundation for deeper understanding. Critics labeling my insights as "too basic" miss the point—it's a signal of growth potential. Sticking to fundamentals at this stage sets me up to tackle more complex topics down the line. If I'm already advanced and still presenting basic concepts, that would indeed be concerning. Focusing on low-hanging fruit allows me to steadily build my knowledge and skills without feeling overwhelmed. It's a strategic approach to learning and mastering more intricate ideas as I progress.
@quant_feed
Leveraging Rust in Delta 1 MM System: A Timely Production Rollout Amidst Market Challenges
0xLoris @ twitter
orig
We're officially integrating Rust into our Delta 1 mm system after a year of pushing through the bear market. We deployed at 3:50 PM on a Friday before a long weekend. Big thanks to the team for their hard work to make this happen. Zig looks promising, while Redis isn’t hindering our performance. We primarily use Python since network, matching engine, and exchange optimizations dominate our focus—previously, reducing latency by a millisecond wasn't our priority.
@quant_feed
0xLoris @ twitter
orig
We're officially integrating Rust into our Delta 1 mm system after a year of pushing through the bear market. We deployed at 3:50 PM on a Friday before a long weekend. Big thanks to the team for their hard work to make this happen. Zig looks promising, while Redis isn’t hindering our performance. We primarily use Python since network, matching engine, and exchange optimizations dominate our focus—previously, reducing latency by a millisecond wasn't our priority.
@quant_feed
🔥3
Enhancing Mean Reversion Strategies: Adjusting Lookback Periods and Volatility Thresholds
GoshawkTrades @ twitter
orig
Mean reversion strategies can falter during sudden volatility spikes. A straightforward adjustment, such as shortening the lookback period or setting a minimum volatility threshold, can enhance performance significantly. This tweak has been beneficial for improving various strategies. We've assisted over 200 traders in refining their approaches into systematic strategies. For those facing challenges with execution or seeking quant development support, there are solutions available to help automate trading effectively.
@quant_feed
GoshawkTrades @ twitter
orig
Mean reversion strategies can falter during sudden volatility spikes. A straightforward adjustment, such as shortening the lookback period or setting a minimum volatility threshold, can enhance performance significantly. This tweak has been beneficial for improving various strategies. We've assisted over 200 traders in refining their approaches into systematic strategies. For those facing challenges with execution or seeking quant development support, there are solutions available to help automate trading effectively.
@quant_feed
Exploring the Top Craft Beers for Quantitative Analysis in Flavor Profiles
therobotjames @ twitter
orig
Garage beers and shed beers dominate the craft beer conversation right now—both are praised for their quality and distinct character. The community is unanimously on board, with shed beers often considered their close cousins. There's a clear consensus that trying these beers can be a transformative experience, with some even calling certain brands the epitome of luxury in the beer world. Enthusiastic endorsements highlight their exceptional flavors and suggest they stand out among craft options.
@quant_feed
therobotjames @ twitter
orig
Garage beers and shed beers dominate the craft beer conversation right now—both are praised for their quality and distinct character. The community is unanimously on board, with shed beers often considered their close cousins. There's a clear consensus that trying these beers can be a transformative experience, with some even calling certain brands the epitome of luxury in the beer world. Enthusiastic endorsements highlight their exceptional flavors and suggest they stand out among craft options.
@quant_feed
😁6
Exploring the Viability of Low Win Rate Strategies in High Frequency Trading
larpcapitalwc @ twitter
orig
There’s substantial potential for decent risk premia in low win rate strategies, provided you execute a high volume of trades. Picture holding a strategy with a mere 10% win rate and only one trade per day; it’s a psychological challenge. After enduring 20 consecutive losses, maintaining confidence to keep trading can be daunting. This highlights the necessity of trade frequency to capture the risk premium—low win rates can still be profitable if the number of trades compensates for the losses. Emphasizing mindset and resilience is crucial; traders must prepare for long losing streaks while trusting their system.
@quant_feed
larpcapitalwc @ twitter
orig
There’s substantial potential for decent risk premia in low win rate strategies, provided you execute a high volume of trades. Picture holding a strategy with a mere 10% win rate and only one trade per day; it’s a psychological challenge. After enduring 20 consecutive losses, maintaining confidence to keep trading can be daunting. This highlights the necessity of trade frequency to capture the risk premium—low win rates can still be profitable if the number of trades compensates for the losses. Emphasizing mindset and resilience is crucial; traders must prepare for long losing streaks while trusting their system.
@quant_feed
Understanding Assumptions and Best Practices for Candlestick Close Prices
crypto_hades @ twitter
orig
1. When calculating candlestick close prices, relying solely on the last trade price can lead to inaccuracies, especially in low liquidity environments like trading shitcoins.
2. Consider alternatives such as midprice, microprice, VWAP, or TWAP for a more accurate representation of close prices, as outdated last trade prices can skew results.
3. Accurate close prices are crucial if you're developing mean reverting strategies on short time frames, like 1-minute candles, where last trade prices are essentially worthless.
4. The main goal should be to ensure that your researched signals are executable at reasonable costs. However, using advanced aggregation methods like microprice necessitates extensive order book data and robust infrastructure that's often impractical for bar-based research.
5. To determine if your strategy is viable, implement a close-to-fill measurement that compares your computed close price with the volume-weighted average fill price upon trade execution.
6. This method allows you to effectively track discrepancies and supports backtesting by providing real execution data rather than arbitrary assumptions for slippage.
7. While similar to markouts, close-to-fill measurements are essential for non-continuous signals, especially when there's time lag between close and execution, as price movements can be significant during that delay.
8. Ultimately, combining close-to-fill analysis with markouts helps identify optimization opportunities in execution, including tracking close-to-signal latency.
@quant_feed
crypto_hades @ twitter
orig
1. When calculating candlestick close prices, relying solely on the last trade price can lead to inaccuracies, especially in low liquidity environments like trading shitcoins.
2. Consider alternatives such as midprice, microprice, VWAP, or TWAP for a more accurate representation of close prices, as outdated last trade prices can skew results.
3. Accurate close prices are crucial if you're developing mean reverting strategies on short time frames, like 1-minute candles, where last trade prices are essentially worthless.
4. The main goal should be to ensure that your researched signals are executable at reasonable costs. However, using advanced aggregation methods like microprice necessitates extensive order book data and robust infrastructure that's often impractical for bar-based research.
5. To determine if your strategy is viable, implement a close-to-fill measurement that compares your computed close price with the volume-weighted average fill price upon trade execution.
6. This method allows you to effectively track discrepancies and supports backtesting by providing real execution data rather than arbitrary assumptions for slippage.
7. While similar to markouts, close-to-fill measurements are essential for non-continuous signals, especially when there's time lag between close and execution, as price movements can be significant during that delay.
8. Ultimately, combining close-to-fill analysis with markouts helps identify optimization opportunities in execution, including tracking close-to-signal latency.
@quant_feed
Enhancing Feature Diversity: Insights from Early Quantitative Experimentation
BeatzXBT @ twitter
orig
Diving into advanced feature engineering, I've leveraged new datasets to diversify my models, though the performance hasn’t yet yielded significant alpha. Shared code for reproducibility; though it's pretty basic, it's user-friendly. My initial features from 6 months ago laid the groundwork for subsequent iterations, now far more sophisticated but proprietary. This journey has been more educational than profitable, appreciating the support from the community. Exploring different strategies continues to be a key focus.
@quant_feed
BeatzXBT @ twitter
orig
Diving into advanced feature engineering, I've leveraged new datasets to diversify my models, though the performance hasn’t yet yielded significant alpha. Shared code for reproducibility; though it's pretty basic, it's user-friendly. My initial features from 6 months ago laid the groundwork for subsequent iterations, now far more sophisticated but proprietary. This journey has been more educational than profitable, appreciating the support from the community. Exploring different strategies continues to be a key focus.
@quant_feed
Understanding the Different Types of PCA: A Simplified Guide
PtrPomorski @ twitter
orig
PCA can often create confusion, but it’s crucial to distinguish between its variants. First, standard PCA is exclusively linear. KernelPCA introduces non-linearity by utilizing a selected kernel, expanding analytical capabilities. For massive datasets, IncrementalPCA serves as a linear PCA alternative that processes data in chunks. Lastly, SparsePCA focuses on feature selection, promoting interpretability by returning a sparse solution. Understanding these distinctions is fundamental for effective data analysis and model selection.
@quant_feed
PtrPomorski @ twitter
orig
PCA can often create confusion, but it’s crucial to distinguish between its variants. First, standard PCA is exclusively linear. KernelPCA introduces non-linearity by utilizing a selected kernel, expanding analytical capabilities. For massive datasets, IncrementalPCA serves as a linear PCA alternative that processes data in chunks. Lastly, SparsePCA focuses on feature selection, promoting interpretability by returning a sparse solution. Understanding these distinctions is fundamental for effective data analysis and model selection.
@quant_feed
👍3
Exploring the Predictive Power of Private Equity Data on Public Equity Returns
quantseeker @ twitter
orig
Recent findings indicate that private equity deal data from FactSet can be a valuable predictor of public equity returns, encompassing both general market performance and specific sector outcomes. This underscores the interconnectedness of private and public market dynamics, implying that savvy investors can leverage this data for strategic advantage. Regular analysis and insights into quant investing, shared weekly, can help sharpen this investment edge.
@quant_feed
quantseeker @ twitter
orig
Recent findings indicate that private equity deal data from FactSet can be a valuable predictor of public equity returns, encompassing both general market performance and specific sector outcomes. This underscores the interconnectedness of private and public market dynamics, implying that savvy investors can leverage this data for strategic advantage. Regular analysis and insights into quant investing, shared weekly, can help sharpen this investment edge.
@quant_feed
Understanding the Fed Dot Plot: A Critical Look at Economic Projections and Their Accuracy
gametheorizing @ twitter
orig
The Fed's Economic Projections often miss the mark, illustrating that their members struggle to accurately predict their future decisions. This reflects a broader societal issue, where honesty and transparency are compromised for the sake of managing expectations. The Summary of Economic Projections (SEP) isn't genuinely about forecasting; it serves more as a tool to influence markets and shape business sentiment through media engagement.
The jargon used by the Fed—akin to a complex dialect—has become familiar territory for me over the years. I've adeptly interpreted their communications, achieving a flawless track record in predicting their moves in 2022. As I gear up to translate their language again in 2023, I'm maintaining significant positions with substantial CME open interest for upcoming rate decisions. My strategic foresight remains grounded in a deep understanding of the Fed's narrative and intentions.
@quant_feed
gametheorizing @ twitter
orig
The Fed's Economic Projections often miss the mark, illustrating that their members struggle to accurately predict their future decisions. This reflects a broader societal issue, where honesty and transparency are compromised for the sake of managing expectations. The Summary of Economic Projections (SEP) isn't genuinely about forecasting; it serves more as a tool to influence markets and shape business sentiment through media engagement.
The jargon used by the Fed—akin to a complex dialect—has become familiar territory for me over the years. I've adeptly interpreted their communications, achieving a flawless track record in predicting their moves in 2022. As I gear up to translate their language again in 2023, I'm maintaining significant positions with substantial CME open interest for upcoming rate decisions. My strategic foresight remains grounded in a deep understanding of the Fed's narrative and intentions.
@quant_feed
Reflecting on My First Day in the FX Market: A Journey Beginning with Harlow Meyer Savage
salr_nyc @ twitter
orig
December 12, 1988 marked my entry into the FX market, kicking off with a successful navigation of the essentials, including finding the toilets and attending the company Christmas party.
Reflecting on that time, I remember the HFT operators actively engaging at the Harlow Meyer Savage USDJPY desk in 2 WTC. It's fascinating how the brokers on that desk often held larger positions than many traders in banks due to the absence of balance sheets, a factor that caught the regulators' attention and led to the push for everyone to operate from screens.
Notably, the talent pool was dynamic; many adept spot brokers transitioned smoothly into FX spot trading roles at banks. It's intriguing to consider the comparative approach of players like Alameda, which didn't seem markedly more strategic to me. In this evolving landscape, I assert that success is in my hands to define.
@quant_feed
salr_nyc @ twitter
orig
December 12, 1988 marked my entry into the FX market, kicking off with a successful navigation of the essentials, including finding the toilets and attending the company Christmas party.
Reflecting on that time, I remember the HFT operators actively engaging at the Harlow Meyer Savage USDJPY desk in 2 WTC. It's fascinating how the brokers on that desk often held larger positions than many traders in banks due to the absence of balance sheets, a factor that caught the regulators' attention and led to the push for everyone to operate from screens.
Notably, the talent pool was dynamic; many adept spot brokers transitioned smoothly into FX spot trading roles at banks. It's intriguing to consider the comparative approach of players like Alameda, which didn't seem markedly more strategic to me. In this evolving landscape, I assert that success is in my hands to define.
@quant_feed
Human Incentives Over Sophisticated Models: Insights from Recent Findings on Risk Management
__paleologo @ twitter
orig
The critique centers on the flawed analysis of financial incentives rather than misaligned risk models. The author argues that human behavior driven by poor incentives led to significant errors in judgment. The referenced report is characterized as akin to a Russian absurdist novel, lacking depth and quality in financial journalism. It’s disappointing that such poor standards persisted in discussions that deserve more rigor and insight.
@quant_feed
__paleologo @ twitter
orig
The critique centers on the flawed analysis of financial incentives rather than misaligned risk models. The author argues that human behavior driven by poor incentives led to significant errors in judgment. The referenced report is characterized as akin to a Russian absurdist novel, lacking depth and quality in financial journalism. It’s disappointing that such poor standards persisted in discussions that deserve more rigor and insight.
@quant_feed
😢1