G-ProphetBeen experimenting with transformer architectures for time-series prediction. Attention mechanisms capture long-range dependencies in price data that LSTMs miss. Early results are promising but overfitting remains the challenge. #machinelearning #quant
MarketForces Africa#Quant (#QNTUSD) gained 8% to $80 in early trading on Friday, significantly outpacing the broader market, which is up just over 1%. Quant is gaining attention for its technical resilience and enterprise progress while the broader market stumbles
https://dmarketforces.com/quant-gains-8-on-robinhood-listing-announcement/
Backtest result worth sharing:
Combined opening-range breakout signals with pre-market news sentiment polarity scores on $COIN. Neither signal alone was particularly clean. Together, the edge was meaningful.
Current model output: +21.9% move predicted over 7 days, 58% confidence. Not high conviction, but directionally consistent with the technical setup.
Methodology thread below. Curious if anyone has tried layering sentiment polarity differently — especially around earnings windows.
Marius (windsheep)https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6344658
The Sharpe Stability Ratio: Temporal Consistency of Risk-Adjusted Performance
Rxiv mechanobio📰 "Geometric Learning Dynamics"
https://arxiv.org/abs/2504.14728 #Quant-Ph #Dynamics #Q-Bio.Pe #Matrix #Cs.Lg
https://arxiv.org/abs/2504.14728 #Quant-Ph #Dynamics #Q-Bio.Pe #Matrix #Cs.Lg
Geometric Learning Dynamics
We present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship $g \propto κ^α$ between the metric tensor $g$ in the space of trainable variables and the noise covariance matrix $κ$. The quantum regime corresponds to $α= 1$ and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to $α= \tfrac{1}{2}$ and describes very fast machine learning algorithms. The equilibration regime corresponds to $α= 0$ and describes classical models of biological evolution. We argue that the emergence of the intermediate regime $α= \tfrac{1}{2}$ is a key mechanism underlying the emergence of biological complexity.
AI forecasting is most useful as a probabilistic decision layer. At G-Prophet, signal direction, confidence calibration, and volatility context matter more than single-number certainty. Method first, hype later. https://www.gprophet.com #AI #Quant
Repeatability matters more than headline accuracy. A useful AI workflow improves consistency under uncertainty rather than selling prediction theater. https://www.gprophet.com #Quant #AI
Repeatability matters more than headline accuracy. A useful AI workflow improves consistency under uncertainty rather than selling prediction theater. https://www.gprophet.com #Quant #AI
📰 "Localized intrinsic bond orbitals decode correlated charge migration dynamics"
https://arxiv.org/abs/2603.10105 #Physics.Chem-Ph #Physics.Comp-Ph #Dynamics #Quant-Ph #Matrix
https://arxiv.org/abs/2603.10105 #Physics.Chem-Ph #Physics.Comp-Ph #Dynamics #Quant-Ph #Matrix
Localized intrinsic bond orbitals decode correlated charge migration dynamics
For decades, scientists have studied the intricate charge migration dynamics, where after ionization a localized charge distribution ("hole") migrates across the molecule on a femtosecond timescale. This has the potential for controlling electrons in molecules, yet a comprehensive understanding of the many aspects of charge migration is still missing. In this work, we analyze charge migration using an extension of localized intrinsic bond orbitals (IBOs). These orbitals lead to a compact representation of the dynamics and map the complex, correlated many-electron charge migration to chemical concepts such as curly arrows and orbital-orbital interactions. By analyzing multiple challenging scenarios, we show how IBOs enable us to identify key mechanisms in charge migration. For example, we show that different mechanisms are responsible for converting a $π$-shaped hole to a $σ$-shaped hole and vice versa. We explain these in terms of hyperconjugation interactions and configurations that couple orbitals with different symmetries. We further demonstrate how IBOs can be used to find molecules with high charge migration efficiency. We carry out all simulations using an efficient set up of the time-dependent density matrix renormalization group (TDDMRG), correlating as many as 45 electrons in 50 orbitals. We believe that our results will be useful to design future experiments. The proposed IBO analysis is applicable to other types of real-time electron dynamics and spectroscopy.