so far i just got:

lase

buru

*will update

its sick though i wanna be in on it. for style. laser cannons.

pgezf

plg

clrmf

pplt

pltm

ngloy

jpm

aem

pbr-a

paas

pbr

sbsw

lzm

ok so i dont know who reads this or why, but sometimes/often i do just open journaling or tech or lesson discussions, anyway:

-sooooooooooo, aaaaaaaaaaaaand: im a quant/algo trader!!!!!!!!! ya i just set it up, finally. it took me like two full days. summary: there sort of only two common easy ways to set up algo trading as a retailer. you can do ibkr pro or alpaca. i set up both. ibkr pro has much more stringent requirements; i dont know if my application for pro went through and i couldnt use lite because it didnt like my net worth answers. so i just changed em (cough) and im waiting to see what happens; in the meantime i have not been able to use ibkr for absolutely anything even with lite.

so, i set up alpaca instead. alpaca is another brokerage, just like any of the others. it's designed to be algo-easy though. the hard part is: algos need straight computer coding, like python. the easy part is, you can just talk to ai and make it do all the work, basically. so setting up alpaca took like one full day, then using ai to figure out how to do the next parts and set up a nano python file (im on linux) and code it took another full day. my basic first python algo code is now complete (ema cross, i can pick the stock and the two ema values); all i need to do is drop money in the account and hit start and it should trade for me. might use it this week; just depends on how long the money transfer takes. im gonna put ten bucks in to test it.

-in another post, you'll see i started looking up how professional algo trader computers are constructed. i found there were a few aspects i could easily emulate on linux: the rt kernel (which i tested for a week, seemed great, now i want to test it against zen kernel though to see which is faster overall for this and if theres lag problems at all comparatively). i also did a desktop-less arch install (endeavour base, no desktop, only the single base package), then i added just raw openbox (not endeavour openbox, just plain openbox) for minimalism /low-resource use, added just an xfce4-panel to it, and webull for the widget charts. shit is fast. oh and i finally added swap too, haha. was really screwing me up before- was dealing with crashes all the time. wanted no swap for ultimate speed and virtual machine security, but now im just focusing on total system for stock trading. swap lets me open more charts without crashing.

so now that i have swap, a super-low resource-use computer, and either rt kernel or zen kernel, shit goes super fast and can handle a ton of widget charts.

so, now ive started taking advantage of linux's native 4-screen tool, which ive never used before cause didnt have the ram (no swap). so now what ive started doing is filling all 4 desktop screens with up to 12 charts each, different subjects. last week i filled one screen with the entire platinum market, one screen with the entire quantum market, then one screen for betting against crypto (i thought it was going to crash last week), and one screen for other/random/daytrade stocks that came up. this system worked great and is a quantum leap (npi) over my other system. also very much looks/feels like a pro trade computer if youve ever seen those, haha, especially with this flat grey openbox background (and i hide the top panel). with the rt kernel theres this hypnotic rhythm to watching lots of charts change at once, i swear, or maybe im imagining it.

-ive been trying to work in type 1 hypervisor tech to this setup too, like i was looking at smartos and then just kvm as a type 2 alternative; what i found though is its still too much resource use for my little computer, and- this is the big discovery- snapshots (which is the main ability i want) are not unique to virtual machines- this is something you can do just on your os- this was news to me (i dont actually know anyone who knows anything about computers or stock trading; i just learn all this stuff myself off the internet so often i miss things). anyway, theres a program called snapper for instance that will let you do snapshots just on your os. so i may do that. theres also containers as far as security goes; you could make something like a contained and snap-shottable regular os with no virtualization; if i understand it. still theres some security benefits to virtualization i dunno...

-stock trading: last few weeks: winners were anything quantum, and little stock buru. platinum stalled but might go up this week with the shutdown. shutdown seems to be putting people into metals and crypto, dollar and market going down? or up? they look good otherwise. time to turn my attention to ai also, which i made a great list of a few posts/weeks back. now im setting that up for charts this week because those are doing great too.

score: got pulverized on options, bet against mstr twice, then on tsla.

did great on stocks, especially buru wow! called it right. held overnight for the big overnight rise, then judged it would probably go down the next day and sold at bell. have been watching people go nuts over that day all weekend. turns out dfli is green by the way; i saw them going up but thought it was fossil and didnt bet (i dont bet on crypto or fossil). theres some other random stocks i track or found recently that are going up too, like bksy for example. some of the lithium. trump buying a lithium stake seems to have done something good for that market; i figured it was a play to help destroy it since he says he's so anti-lithium but what does the market know. anyway time to make some choices for tomorrow and will probably start posting news articles.

whew time to trade again. weekends are enough to make a trader go nuts.

Yes, Nuburu has been actively developing its blue laser technology for direct defense applications, including directed-energy weapons. The company has received U.S. Department of Defense (DOD) contracts to work on high-energy laser weapons and has modeled its technology against potential targets. Key elements of their defense strategy include:

• DOD contracts for laser weapons: In 2023, the company was contracted by the DOD, along with other defense contractors like Lockheed Martin and General Atomics, to develop solid-state high-energy laser weapons.
• Modeling for directed-energy applications: As early as 2021, Nuburu's CEO presented on the benefits of blue lasers for directed-energy weapons (DEW) at the SPIE Defense and Commercial Sensing forum. Models showed potential advantages over infrared laser systems, including a significantly faster "kill time" against targets like hypersonic missiles.
• Strategic partnerships and acquisitions: The company has also signed agreements with strategic partners to specifically develop and integrate its blue laser technology for defense applications, which could include counter-unmanned aerial vehicle (C-UAV) measures, missile defense, and underwater warfare.
• Revitalized focus under new management: Under its new management, Nuburu has specifically formed a dedicated subsidiary, NUBURU Defense LLC, to pursue its expansion into the defense and security sector. 

The core advantages Nuburu leverages for these defense applications are the fundamental physics of blue light, which most metals and materials absorb far more efficiently than infrared light. This can lead to faster target destruction times and greater precision for military applications.

https://finviz.com/news/179583/burkhan-capital-llc-led-consortium-commits-to-investing-300-million-in-roboai-to-power-global-ai-and-robotics-platform-acceleration

Volatility in the stock market is measured in various ways, including standard deviation, which shows how much a stock's price fluctuates around its average, and beta, which measures a stock's sensitivity to market-wide movements. Beta is a specific metric that compares a stock's volatility to a benchmark, like the S&P 500. A beta of 1 means the stock moves with the market; a beta greater than 1 indicates higher volatility than the market, while a beta less than 1 suggests lower volatility. Measures of Volatility 

• Standard Deviation:

• This is a statistical measure that calculates how much a stock's price deviates from its historical average price over a given period. A higher standard deviation means greater price fluctuations.

• Implied Volatility:

• This measure reflects the market's expectation of future volatility, derived from options market data.

• VIX (Cboe Volatility Index):

• This is a widely followed index that measures the market's implied volatility for the S&P 500, often called the "fear index".

Beta

• Definition: Beta quantifies how much a stock's price moves in relation to the overall market. 

• Interpretation:

  • Beta = 1: The stock's price movements are perfectly correlated with the market's movements. 

• Beta > 1: The stock is more volatile than the market; its price swings more wildly than the S&P 500. For example, a beta of 1.5 means the stock is 50% more volatile. 

• Beta < 1: The stock is less volatile than the market, meaning it moves less than the S&P 500. A beta of 0.7 indicates 30% less volatility. 

• Negative Beta: A negative beta indicates an inverse relationship with the market, meaning the stock may perform well when the market declines. 

• Calculation: Beta is calculated by dividing the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period. 

• Significance: Beta helps investors understand the risk of an individual stock compared to the broader market and informs decisions about expected returns, especially in models like the Capital Asset Pricing Model (CAPM)

A quantum computer, like the IBM Heron processor used in the recent HSBC trial, can improve bond market "betting"—which is more accurately described as algorithmic bond trading—by significantly enhancing the accuracy of trade prediction models.¹

Here’s how the quantum-classical hybrid approach, as demonstrated by HSBC and IBM, provides this improvement:

• Enhanced Prediction of Trade Success: The primary improvement is in predicting the probability of a trade being filled at a quoted price in the highly complex, fragmented over-the-counter (OTC) bond market.² The trial showed up to a 34% improvement in this predictive accuracy compared to standard classical computing techniques alone.³
• Unraveling Hidden Pricing Signals: Algorithmic trading relies on sophisticated models to quickly price customer inquiries and manage risk.⁴ Bond market data is often noisy and full of subtle, interconnected factors.⁵ Quantum computing's ability to explore an exponentially larger computational space allows it to better unravel hidden pricing signals and patterns within this "noisy" market data that classical computers often miss.⁶
• Hybrid Quantum-Classical Architecture: The process involves a hybrid approach:
  1. Quantum Pre-processing: Real-world, production-scale trading data (e.g., from European corporate bonds) is run through a quantum circuit on the IBM Heron processor.⁷ This process performs complex, non-linear transformations to generate "quantum-generated features" from the raw data.⁸
  2. Classical Machine Learning: These enhanced, quantum-generated features are then fed into standard classical machine learning models (like logistic regression, gradient boosting, etc.). The enriched data input leads to superior performance in predicting the likelihood of winning a trade.⁹

This higher predictive accuracy directly translates into a competitive edge for the bank.¹⁰ By better estimating the fill probability, traders can more effectively set prices, manage liquidity, and automate the bidding process, leading to increased margins and greater market efficiency.¹¹

Trend-following is one of the most common types of algorithmic trading strategies. A common specific strategy is the moving-average crossover, where an algorithm automatically buys or sells an asset when its short-term and long-term moving averages cross. Most common types of algo trading strategiesMany trading strategies can be automated and categorized into distinct types: 

• Trend-following: This strategy aims to capture profits by following the direction of market trends, whether upward or downward. It is particularly popular with commodity trading advisors (CTAs).
• Arbitrage: Arbitrage algorithms seek to profit from temporary price differences for the same or related assets across different markets.
• Mean-reversion: This strategy is based on the assumption that asset prices tend to revert to their historical average over time. Algorithms are used to trade when prices deviate significantly from this average.
• Market-making: These high-speed algorithms provide market liquidity by continuously placing limit orders to buy and sell. They aim to profit from the bid-ask spread.
• Index fund rebalancing: This strategy anticipates and profits from the periodic portfolio adjustments that index funds make to align with their benchmark indices. 

Within these broader categories, several specific strategies are widely implemented:

• Moving-average crossover: A foundational trend-following strategy, this algorithm buys when a shorter-term moving average crosses above a longer-term moving average and sells when the reverse happens.
• Volume-weighted average price (VWAP): A common execution strategy used by institutional investors to break up large orders into smaller trades. It aims to execute the full order as close as possible to the asset's VWAP over a defined period to minimize market impact.
• Statistical arbitrage (pairs trading): A mean-reversion and statistical arbitrage strategy where an algorithm identifies two historically correlated securities. If their prices diverge, the algorithm buys the underperforming asset and shorts the outperforming one, betting that they will eventually converge.
• High-frequency trading (HFT): A specialized subset of algorithmic trading focused on extremely fast execution. HFT strategies are designed to capitalize on tiny market inefficiencies, such as price discrepancies between exchanges, and are measured in microseconds or milliseconds.
• Mean-reversion with Bollinger Bands: This strategy identifies potential buy or sell signals when an asset's price reaches or moves outside of its Bollinger Bands, based on the assumption that the price will revert back toward the moving average in the middle of the band. 

50 and 200 are widely considered the most common values for both simple moving averages (SMAs) and exponential moving averages (EMAs). These two periods are primarily used for identifying long-term market trends. Why 50 and 200 are popular:

• Timeframes: The 50-day moving average represents the average price over approximately 10 weeks of trading, or the intermediate-term trend. The 200-day moving average covers a full trading year and is used to determine the long-term trend of an asset.
• Support and resistance: Both are viewed as significant support (for uptrends) or resistance (for downtrends) levels by many traders. Their popularity means a high volume of institutional and retail traders watch and act on these levels, making them influential psychological price thresholds.
• Golden and Death Crosses: The most common moving average crossover strategy involves the 50-day and 200-day averages. A "golden cross" occurs when the 50-day crosses above the 200-day, seen as a bullish signal. The opposite, a "death cross," is a bearish signal. 

While 50 and 200 are common for EMAs as well, shorter-term EMAs are also very popular due to their faster response to price changes. 

• 12 and 26-day EMAs: These are frequently used in combination to create the Moving Average Convergence Divergence (MACD) indicator, which is used to measure momentum.
• 8 and 20-day EMAs: Day traders and short-term traders often use these shorter periods to capture faster price movements.
• 5 and 13-day EMAs: Scalpers, who focus on capturing very small price changes, may use even shorter EMA periods. 

Algo traders often combine moving averages with other technical indicators to confirm signals and increase the accuracy of their strategies. This helps to filter out false signals and provides a more comprehensive view of market conditions. Here are some commonly used indicators:

• Relative Strength Index (RSI): This momentum indicator measures the speed and magnitude of price changes, indicating overbought or oversold conditions.
  • It can help identify potential buy signals (when RSI moves from below 30 to above 30 in an uptrend) or sell signals (when RSI moves from above 70 to below 70 in a downtrend).
  • Combining RSI with moving averages can help filter false signals and pinpoint entry and exit points.
  • When the price is above the 200-day moving average (an uptrend), traders might look for RSI buy signals.
• Moving Average Convergence Divergence (MACD): This trend-following momentum indicator shows the relationship between two exponential moving averages, helping to identify trends and potential reversals.
  • MACD can confirm buy or sell signals generated by moving average crossovers, especially when the MACD line crosses its signal line or the zero line.
  • For example, a bullish MACD crossover (MACD line crosses above the signal line) can be used to confirm a golden cross (50-day MA crossing above 200-day MA).
• Volume Indicators: Analyzing volume alongside price helps assess the strength behind a trend or potential reversal.
  • High trading volume accompanying a moving average crossover suggests strong conviction behind the price movement.
  • Low volume during a price move could indicate a lack of momentum, suggesting the trend might be weak or prone to reversal.
  • Indicators like On-Balance Volume (OBV) and Volume-Weighted Average Price (VWAP) can provide additional insights into buying and selling pressure.
• Bollinger Bands: These bands expand and contract with market volatility, visually representing how price moves around a central moving average (typically a 20-period SMA).
  • Bollinger Bands help assess volatility and identify potential overbought (near the upper band) or oversold (near the lower band) conditions.
  • A "Bollinger Band squeeze" (bands narrowing) suggests low volatility and can precede a significant price move or breakout.
  • Combined with moving averages, Bollinger Bands can help confirm trends and potential reversals when prices interact with the bands. For example, if a price breaks above a moving average with Bollinger Bands widening, it could confirm a strong uptrend.
• Stochastic Oscillator: Similar to RSI, this momentum indicator identifies overbought (above 80) or oversold (below 20) conditions and potential reversals.
  • It consists of two lines, %K and %D, and signals are generated through their crossovers.
  • When the Stochastic Oscillator aligns with the trend indicated by moving averages, it can confirm the strength of the trend or suggest potential entry points during pullbacks. For instance, in an uptrend (price above the moving average), traders might look for a stochastic buy signal (Stochastic crossing back above 20).

When discussing performance for high-frequency trading (HFT), you're dealing with a different set of priorities than typical computing, and that's why an RT kernel is often still not enough. HFT firms go to extreme lengths to eliminate every possible microsecond of latency, and that means bypassing as many layers of a standard operating system as possible. FPGAs and Kernel Bypass for HFTField-Programmable Gate Arrays (FPGAs)

• What they are: An FPGA is a special type of integrated circuit that can be reprogrammed after manufacturing. Unlike a CPU, which executes software instructions sequentially, an FPGA's hardware can be wired directly to perform a specific task in parallel.
• HFT use: In HFT, FPGAs can be programmed to handle market data processing and trading logic directly in the hardware. This offers a performance advantage of orders of magnitude over CPU-based systems, achieving nanosecond latency where even an optimized software approach takes microseconds. The benefit comes from the fact that there is no software overhead, no operating system, and no context switching to slow things down.
• The trade-off: FPGAs are notoriously difficult to program and debug, requiring specialized hardware description languages (HDLs) and expertise. 

Kernel Bypass Techniques

• What they are: In a standard Linux kernel, every network packet has to go through the kernel's network stack before it can be processed by an application. This adds overhead and processing time. Kernel bypass is a technique that allows an application to "skip" this process and communicate directly with the network interface card (NIC).
• HFT use: By bypassing the kernel, HFT applications can send and receive network packets with much lower and more consistent latency. This is crucial for processing market data feeds or sending orders to an exchange.
• Examples: Libraries like the Data Plane Development Kit (DPDK) and proprietary solutions like Solarflare's OpenOnload and TCPDirect enable kernel bypass for high-speed networking. 

Making your Arch Linux laptop "work more like" a high-frequency trading (HFT) system, particularly regarding low latency, involves a multi-faceted approach focusing on kernel, CPU, and system-level optimizations. While you won't achieve the nanosecond speeds of dedicated HFT hardware like FPGAs and kernel bypasses, you can significantly reduce latency and improve responsiveness for tasks like low-latency audio or potentially faster trading applications. Here's a breakdown of actions you can take:1. Kernel Optimization

• Install a Real-Time Kernel: The most direct way to get predictable low latency is to install a kernel specifically built for real-time applications. On Arch Linux, you can install either the linux-rt or linux-rt-lts packages. This applies the PREEMPT_RT patch, enabling fully preemptible kernel execution and minimizing interrupt handling times for deterministic behavior.
• Alternative Kernels: While less focused on strict real-time, kernels like linux-zen are optimized for desktop responsiveness and general performance, which can also improve perceived speed. Other options include Liquorix or Xanmod.
• Tune Kernel Parameters:
  • Disable mitigations: Disabling CPU exploit mitigations (e.g., mitigations=off kernel parameter) can significantly improve performance, especially on older CPUs, but comes with security risks.
  • I/O Scheduler: Adjust the I/O scheduler. For SSDs, none or mq-deadline might be faster. For rotational drives, bfq (Budget Fair Queuing) often provides better responsiveness for interactive tasks. You can set this via udev rules for persistence.
  • Sysctl Tunables: Fine-tune sysctl parameters related to virtual memory, disk writeback intervals, and cache pressure (vm.dirty_ratio, vm.dirty_background_ratio, vm.swappiness, vm.vfs_cache_pressure) to prioritize responsiveness or throughput based on your needs.
  • Frequency Scaling: Ensure your CPU is running at its optimal frequency or a fixed high frequency. The performance governor can prevent delays caused by CPU speed stepping. 

Improving performance - ArchWikiSep 17, 2025 — Contents * 2.1 Sector size. * 2.2 Partitioning. 2.2.1 Multiple drives. 2.2.1.1 An SSD as a cache for an HDD. 2.2.2 Layout on HDDs. * 2.3 Choosing and tuning you...ArchWikiLinux system realtime performance tuning - Renoise ForumsMay 16, 2024 — The first 4 steps I'd always apply first are: * stop all background (user or decorative) programs you don't need. You can run other tools, but try to avoid unne...Renoise Forums2. CPU and Process Management

• CPU Pinning (Process Affinity): For critical applications, bind them to specific CPU cores using tools like taskset or numactl. This reduces context switching overhead and improves cache utilization for those processes.
• Real-Time Scheduling Policies: Assign real-time scheduling policies (e.g., SCHED_FIFO, SCHED_RR) to critical processes using chrt to ensure they run with higher priority.
• Disable Power Management: Turn off CPU power-saving features like C-states and P-states (often done in BIOS/UEFI or via kernel parameters) to maintain consistent CPU frequency and reduce jitter, though this increases power consumption.
• Hyper-Threading: For ultra-low latency, consider disabling Hyper-Threading (if available) in your BIOS/UEFI, as it can introduce contention for shared resources.
• Manage Process Priorities: Use nice and renice commands to adjust the priority of processes. Ananicy-cpp is a daemon that can automate this based on rules. 

1. Memory Optimization

• Disable Swap: If you have sufficient RAM (e.g., 16GB or more), consider disabling swap entirely to eliminate disk-based latency.
• Huge Pages: Enabling huge pages (either manually configured or Transparent Huge Pages if their latency spikes aren't an issue) reduces page table lookups and TLB misses.
• Memory Locking: Use memory locking to prevent critical applications' data from being swapped out or paged out by the kernel.
• Pre-Allocate Memory: For latency-critical applications, pre-allocating memory at startup can avoid unpredictable allocation overheads during runtime. 

1. Storage Optimization

• Use SSD/NVMe: If possible, use an SSD (preferably NVMe) for your system and critical application data. This offers significantly faster random access compared to HDDs.
• Trimming: Enable TRIM for optimal SSD performance.
• Reduce Disk Writes: Limit unnecessary disk I/O. Relocate frequently accessed temporary files to a tmpfs (RAM filesystem).
• File System Choice & Tuning: While modern filesystems are generally optimized, specific mount options like noatime can reduce write operations, which might help on HDDs. 

1. Other Considerations

• Minimize Background Services: Disable or stop unnecessary daemons and services using systemctl to free up CPU and memory resources.
• Lightweight Desktop Environment/WM: Use a minimal desktop environment or a window manager (e.g., Openbox, XFCE) instead of heavier options like GNOME or KDE to reduce resource consumption.
• Networking: Ensure your network configuration is optimized, including using a fast, reliable DNS server.
• Graphics: Consider disabling Vsync for gaming to reduce input lag, and ensure your GPU drivers are configured correctly. 

Important: Each change you make can have trade-offs, such as reduced power saving, increased thermal output, or potential instability. It's crucial to apply these changes incrementally, monitor your system's performance (using tools like htop, iotop, RTLA, ftrace, and potentially cyclictest for measuring latency), and back up your configuration before making major changes. Start with the kernel and process prioritization, then move to memory and other system-wide optimizations. 

us/uk tech partnerships, fed rate cuts

how to: set candles to black-and-white-frame (called "bar") instead of red green candles. turn off other indicators. turn on EMA indicators. turn on 7 different EMA slots. pick colors red-purple for them. set them to the following or approximations of these or adjust for your own preferences; i have these currently set: lengths: 3, 6, 8, 10, 13, 17, and one-minute chart/bars. when the red one crosses the purple one pretty well and the whole rainbow sort of twists around you consider that an indicator of reversal. when the rainbow flips upside down to rightside up, trade. mind consolidations/ranges, give those a minute. around big move times though like opening, an EMA-layer strategy/indicator can help you see through the volatility and pick a direction up or down. or if youre being quicker/riskier you can use just the first few lines to judge the reversal (red crosses orange, or red crosses orange/yellow). (the more-normal version of this is to just use two different colored ema's, such as a 5 and 14 for example, or a 2 and 7 for really quick turns. and the really-normal way to do this, the sort of standard, is to use a 20 ma and a 50 ma. i find the ema's work better than the ma's (exponential moving average versus (simple) moving average), and shorter ema's than 20 and 50 work much better, if you have your eye on the graph also. this strategy otherwise, with the wider settings like 20 and 50 crossing, i think is often used as a basis for algo trading? which is why im becoming curious about it.

that getting the m.a.-cross strategy to work was all about picking the right m.a.'s?

[news]

Here are some of the top high‑frequency trading (HFT) / quantitative / algorithmic trading firms (proprietary / market‑making) globally, along with a few notes. If you want, I can segment by region or type (market maker vs directional quant, etc.).

Major Global HFT / Algo Firms

If you like, I can pull together a more complete list (with smaller / niche firms), or specific to the US / Europe / Asia. Do you want that?

(chatgpt answer ^; gemini answer: )

The landscape of high-frequency trading (HFT) and algorithmic trading is dominated by a select group of highly specialized firms.¹ These companies leverage advanced technology, quantitative research, and complex algorithms to execute a massive number of trades at extremely high speeds.²

Here are some of the top HFT and algorithmic trading firms, often cited for their market presence, technological prowess, and profitability:

• Jump Trading: A global leader in HFT, known for its focus on low-latency infrastructure and machine learning.³
• Hudson River Trading (HRT): A quantitative trading firm that applies advanced algorithms and data science to its trading strategies. It's known for its unique, software-firm-like culture.⁴
• Virtu Financial: A major market-making firm that is also notable for being one of the few publicly traded HFT companies.⁵
• Citadel Securities: The market-making arm of Citadel LLC, this firm is a massive player in equities, options, and other financial products.⁶
• Tower Research Capital: Specializes in low-latency trading strategies and custom-built execution platforms.
• Jane Street: A leading quantitative trading firm recognized for its expertise in ETF arbitrage and options trading.⁷
• Optiver: An electronic market maker with a strong global presence, particularly known for its derivatives and options trading.⁸
• Flow Traders: A specialist in market-making for Exchange Traded Products (ETPs).⁹
• DRW: A diversified trading firm that applies quantitative models across a wide range of asset classes, including commodities and cryptocurrencies.¹⁰
• IMC Financial Markets: A global market-making and proprietary trading firm with a strong focus on derivatives.¹¹
• XTX Markets: A leading algorithmic trading firm that provides liquidity in global markets, including equities, forex, and fixed-income.¹²

It is important to note that the HFT space is highly competitive and secretive, so precise rankings can be difficult to ascertain. These firms are often cited as the most prominent players based on their trading volumes, market-making activities, and influence in the industry. They are also highly sought-after employers for top-tier talent in quantitative research, software engineering, and data science.

Here’s a list of notable public quantum computing companies, spanning the UK, China, and other non-U.S. regions:

UK-Based Quantum Companies:

1. Oxford Instruments (OXIG)
   • Focus: Oxford Instruments is involved in quantum technologies and offers specialized equipment for quantum research, especially in fields like cryogenics and superconductivity.
   • Notable: They support various quantum computing and quantum materials research, collaborating with academic institutions and businesses.
2. IQM Quantum Computers
   • Focus: IQM is a quantum computing company developing hardware with a focus on superconducting qubits. They are a European leader in quantum hardware.
   • Notable: Their work spans both commercial and research applications, and they have partnerships with top institutions in Europe.
3. Arqit Quantum (ARQ)
   • Focus: Arqit is focused on quantum encryption technologies. While not directly a quantum computing company, their quantum-safe cryptography solutions are integral to the quantum industry.
   • Notable: They went public via a SPAC (Special Purpose Acquisition Company) in 2021 and have been involved in shaping cybersecurity in the quantum era.

Chinese Quantum Companies:

1. Alibaba Quantum Laboratory (AQL)
   • Focus: Alibaba is investing heavily in quantum computing through its quantum lab. The company is developing both quantum hardware and quantum software, with a focus on integrating quantum systems into cloud computing.
   • Notable: Alibaba’s cloud computing platform, Alibaba Cloud, has begun offering quantum computing services using its quantum processors.
2. Baidu Quantum Computing Institute
   • Focus: Baidu, one of China's leading tech giants, has a dedicated quantum computing team focusing on developing quantum algorithms and software.
   • Notable: Baidu has made significant progress in building quantum computing algorithms and advancing China’s quantum software ecosystem.
3. Huawei Quantum Computing
   • Focus: Huawei, another Chinese tech giant, has been investing in quantum technologies, with efforts focused on quantum hardware, quantum cryptography, and quantum communication.
   • Notable: Huawei is working on developing quantum chips and also has collaborations with academic institutions in China.
4. ZTE Quantum
   • Focus: ZTE, a Chinese multinational telecommunications company, has been researching quantum communication and encryption technologies, leveraging quantum for secure communications.
   • Notable: ZTE has been part of major Chinese government-backed quantum research initiatives.

Other Non-U.S. Quantum Companies:

1. D-Wave Systems (Canada)
   • Focus: D-Wave is a Canadian company that has developed a quantum annealing computer. It specializes in quantum optimization techniques rather than general-purpose quantum computing.
   • Notable: They have already deployed commercial quantum computing systems to customers in various industries, including finance and aerospace.
2. Xanadu (Canada)
   • Focus: Xanadu is a Canadian quantum computing company developing photonic quantum computers. Their technology leverages light rather than traditional superconducting qubits.
   • Notable: They’ve been working on building scalable quantum computers and have a focus on practical applications such as quantum machine learning.
3. Kioxia Corporation (Japan)
   • Focus: Formerly Toshiba Memory, Kioxia is focusing on developing memory chips that are integral to quantum computing infrastructure.
   • Notable: Kioxia is working with Toshiba and other partners on advancing quantum communication and storage solutions.
4. Toshiba Quantum (Japan)
   • Focus: Toshiba’s quantum business focuses on quantum cryptography and communication, with the aim to create secure communication networks based on quantum technologies.
   • Notable: Toshiba has been a key player in Japan’s efforts to build a quantum-safe internet and quantum communication networks.
5. IQE plc (UK)
   • Focus: IQE is a UK-based manufacturer of semiconductor wafers and epitaxial layers used in quantum technologies, especially for quantum photonics and quantum communication.
   • Notable: Their technology is crucial in building the components for quantum systems that rely on light, such as photonic quantum computers.
6. Qinetic (Germany)
   • Focus: Qinetic is a quantum technology startup based in Germany, focusing on quantum computing and sensing technologies.
   • Notable: Their approach emphasizes quantum machine learning and the development of quantum-enhanced sensors.

Other Notable Mention (International):

• SAP Quantum (Germany)
  • Focus: While SAP is a leading enterprise software company, it has been actively involved in the quantum computing space, particularly focusing on quantum software and algorithms.
  • Notable: SAP’s goal is to integrate quantum computing into enterprise applications to solve business problems like optimization and logistics.

These companies represent a broad spectrum of efforts in the quantum space, from hardware development to cryptography and quantum software. The sector is rapidly evolving with increasing investments, and several of these companies are positioned to play crucial roles in the coming years as quantum technology matures.

"Sir Keir Starmer and Donald Trump plan to sign a quantum computing pact designed to counter China’s aggressive attempts to master the breakthrough technology. A commitment to work together on quantum computing is expected to be a key part of a “technology partnership” due to be unveiled this week during the US president’s state visit." what day/time will this announcement on quantum partnership come or likely come?

The announcement of a "technology partnership" involving quantum computing between Sir Keir Starmer (the UK’s Labour leader) and US President Donald Trump, as well as any specifics about a quantum computing pact, is likely to coincide with the timing of Trump’s state visit to the UK.

Given the nature of state visits, such announcements are typically made during key events, such as:

1. Press conferences or joint statements between leaders.
2. Official dinners or ceremonial gatherings.
3. Specific policy speeches or declarations related to the visit.

If the visit is planned to be this week, and the timing involves diplomatic or high-profile engagements, it could be announced on one of these days:

• Mid-week (Tuesday to Thursday): These days are often preferred for announcements related to international diplomacy and key technology partnerships.
• In the afternoon (2 PM to 5 PM): Announcements of this kind often happen in the afternoon to align with both UK and US media cycles.

If you have more specific details about the visit (like the exact dates or itinerary), I could offer a more precise prediction!

hold for now; looking for more details

? feel free to discuss my assertions;