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Nasdaq and Payward, Kraken's parent company, have partnered to develop Nasdaq Equity Tokens and support tokenized equities. Tokenizing financial assets and the movement of money could release tens of billions of dollars in capital …

Nasdaq and Payward, Kraken's parent company, have partnered to develop Nasdaq Equity Tokens and support tokenized equities. Tokenizing financial assets and the movement of money could release tens of billions of dollars in capital locked up as collateral throughout the global financial system, said Nasdaq CEO Adena Friedman in an interview with CNBC at TOKEN2049 in Singapore. Friedman said tokenized Treasurys, equities and money market funds could make collateral more fluid by improving its liquidity. Nasdaq’s growing involvement in tokenization is focused on the systems that allow traditional financial assets to operate on blockchain networks. Advertisement The company is working with Payward, Kraken’s parent company and the infrastructure provider behind xStocks, on an equities transformation gateway. The goal is to connect regulated, permissioned financial infrastructure with permissionless blockchain networks where tokenized equities can circulate. Friedman said institutional interest in tokenization has accelerated over the past year, with the passage of the GENIUS Act, which established a regulatory framework for stablecoins, contributing to the trend. The news moving money, markets, and the world—before your day starts. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Email address Subscribe free We respect your privacy. Unsubscribe anytime. She said the ability to tokenize money could also allow financial institutions to tokenize capital flows. The development is bringing institutional finance closer to retail investors’ demand for 24-hour markets, which Friedman said has put the retail ecosystem about 10 years ahead. Establishing fully round-the-clock financial markets would require more than upgrading trading infrastructure. Friedman said exchange infrastructure is the easiest element to address, while institutions would need to ensure that risk controls, collateral management and system updates can function without the scheduled downtime traditionally associated with market closures. All these processes would need to operate in real time. Nasdaq is using artificial intelligence to help prepare for that shift. The firm has introduced digital agents in its risk management platform that currently provide recommendations and could eventually execute actions more directly. Friedman said AI would be essential to supporting 24/7 trading.

The NVIDIA-backed AI data center operator is turning to private markets after its planned ASX float failed to draw enough buyers Firmus Grid Ltd. was set to stage one of the biggest stock market …

The NVIDIA-backed AI data center operator is turning to private markets after its planned ASX float failed to draw enough buyers Firmus Grid Ltd. was set to stage one of the biggest stock market debuts Australia has seen in decades. Instead, it has walked away from the stage before the curtain went up. The NVIDIA-backed AI data center operator shelved its planned initial public offering around October 8-9, 2026, after failing to line up enough investor demand. The deal had been designed to raise up to A$5.5 billion. Market volatility did the rest. What Firmus was trying to pull off Firmus priced its shares at A$11 each, a level that implied an equity value of approximately A$43.7 billion, or around US$30 billion. Shares were expected to begin trading on the Australian Securities Exchange around October 23, 2026. That debut will no longer happen on schedule. Had it gone ahead, the float would have ranked among Australia’s largest in recent decades. Only Telstra’s landmark 1997 listing would have been bigger. Firmus currently runs two small operational data centers. Investors were being asked to price in a future that, for now, mostly exists in project plans and partnership agreements. Advertisement A valuation that moved fast In April 2026, Firmus raised $505 million at a post-money valuation of $5.5 billion. Four months later, in August 2026, it brought in another $2 billion. That round valued the firm at more than $10.5 billion. AI, tech, and the markets they move—in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Email address Subscribe free We respect your privacy. Unsubscribe anytime. The IPO price then pointed to an equity value of around US$30 billion. Coatue Management and Blackstone are among the names supporting the company. Public market investors, despite earlier strong interest, did not fill the order book to the level Firmus needed. What Firmus actually builds Firmus develops modular AI data centers, which it calls “AI factories.” The company leans on advanced energy and cooling technologies. Its flagship effort is Project Southgate in Australia. Firmus also has operations in Indonesia. Much of that work centers on its collaboration with NVIDIA. The partnership involves the use of up to 170,000 GPUs. Firmus also has partnerships with OpenAI and Meta tied to cloud services and capacity expansion. Why the timing went wrong Firmus cited market volatility as a key factor behind the decision. A deal of this size also leaves little room for error. Raising up to A$5.5 billion in a single offering requires broad participation, not just enthusiasm from a handful of anchor investors. Firmus chose to pull the deal rather than force it through at a lower price or with a thin book. What this means for Firmus and the AI infrastructure trade For Firmus, the immediate plan is to tap private-market funding and other alternatives while it keeps building out its projects. The company has already raised more than $2.5 billion across its two 2026 rounds. The things to watch now are concrete. Investors will be tracking the size and pricing of Firmus’s next private round, progress on Project Southgate and its Indonesian operations, and how quickly its GPU deployment with NVIDIA scales. Either way, Australia’s hoped-for record-adjacent float has been moved to the “maybe later” pile. Telstra keeps its spot at the top of the list, for now.

LumiBot AI Trading AI agents that actually place the trade. Twelve broker integrations, real backtests, and stocks, options, futures, forex, crypto and prediction markets. Most AI trading projects stop at a recommendation. LumiBot sends …

LumiBot AI Trading AI agents that actually place the trade. Twelve broker integrations, real backtests, and stocks, options, futures, forex, crypto and prediction markets. Most AI trading projects stop at a recommendation. LumiBot sends the order. Read this in 中文 · Español · Français · Deutsch · 日本語 · 한국어 · Português · Русский Sixty seconds pip install lumibot lumibot demo That runs a real backtest on free daily data and writes a tearsheet. No API key, no broker account, no configuration. Then make it yours: lumibot init my-bot --template ai # writes an ordinary, editable Strategy subclass lumibot backtest my-bot --days 90 lumibot run my-bot --paper lumibot init writes the same Python you would have written by hand. Nothing is hidden behind the CLI, and you keep an editable file. The strategy class can stay the same. The code that starts it must select a backtest or a broker run. Strategy.backtest(...) always backtests; Trader.run_all() or strategy.run_live() starts a configured broker. Setting IS_BACKTESTING=false alone cannot turn a backtest-only example into a broker runner. Check every AI example's run mode Prefer traditional trading strategies? Use --template python instead. Write your own rules, indicators, and order logic in a normal Strategy subclass. No AI model or model API key is required. Python quickstart · AI quickstart · Python examples · AI examples Why LumiBot? Use Python rules, AI agents, or both. Keep one familiar Strategy lifecycle. Keep one familiar lifecycle. The decision reaches a broker. A deterministic Python gate the model cannot talk past, real broker orders, and a trace you can open. How it works A deterministic Python gate the model cannot talk past, real broker orders, and a trace you can open. How it works Backtest before connecting a broker. Run historical simulations and view trades and results. Run historical simulations and view trades and results. Reuse your strategy across supported brokers. Keep strategy logic separate from broker configuration. Keep strategy logic separate from broker configuration. Start from working examples. Choose stocks, macro, options, or a traditional buy-and-hold strategy. How LumiBot compares AI trading projects have proved that people want agentic trading workflows. Lumibot's edge is that those workflows run inside a real Python trading framework: you can backtest the agent decisions, inspect artifacts, add Python guardrails, paper trade, and connect to brokers without rewriting the strategy. That matters because an AI trading demo is not the same thing as a trading system. Without backtests and broker-aware strategy code, you are mostly trusting prompts. Lumibot lets you iterate faster: test the agent flow on historical data, see what it would have bought or sold, tighten the Python risk checks, then run the same lifecycle in paper or live trading. Compared with AI trading agent projects Project Main angle AI agents / teams Backtest agent decisions Paper/live broker path Deterministic Python strategies Hosted data/deploy/monitoring Lumibot + BotSpot Python strategies, flexible AI trading teams, hybrid guardrails, backtests, brokers, hosted deployment Flexible teams, debates, specialist desks, and deterministic gates Replayable decisions, orders, traces, artifacts, charts, logs Yes: Alpaca, IBKR, Tradier, Schwab, Tradovate, ProjectX, Bitunix, Polymarket, selected CCXT Yes Hosted data, parallel backtests, deployment, monitoring, MCP, alerts, kill switches TradingAgents Multi-agent LLM trading research framework Yes, with a specific research/debate structure Research/demo oriented Not the main focus Limited No ai-hedge-fund Educational AI hedge fund with named investor-style agents Yes, with investor-style personas Demo/backtest oriented Not the main focus Limited No OpenAlice One-person Wall Street agent concept Yes, end-to-end agent concept Emerging/experimental Local/self-run focus Limited No QuantDinger Self-hosted AI quant operating system Yes Yes Crypto, IBKR, MT5, Alpaca Yes Self-hosted Vibe-Trading Personal trading agent Yes Yes Agent trading platform focus Limited Platform-specific AI-Trader Agent-native trading platform Yes Platform focus Platform focus Limited Platform-specific OpenBB Financial data platform for analysts, quants, and AI agents Tooling for agents Not a strategy backtester No broker execution framework No OpenBB workspace/platform Qlib AI-oriented quant research platform Research/ML agents Quant research backtests Limited live focus Research pipelines No See the docs comparison pages for more detail: Lumibot vs TradingAgents, Lumibot vs ai-hedge-fund, Lumibot vs OpenAlice, and Lumibot vs QuantDinger. Compared with backtesting libraries Feature Lumibot Backtrader Freqtrade Zipline Backtesting.py Jesse vectorbt NautilusTrader Hummingbot Same code: backtest + live Yes Yes Yes (crypto) No No Yes (paid) No Yes Yes (crypto) Stocks Yes Yes No Yes Yes No Yes Yes No Options Yes No No No No No No Limited No Crypto Yes Limited Yes No Yes Yes Yes Yes Yes Prediction markets Polymarket trading and backtesting No No No No No No No Limited/no Futures Yes Limited Crypto only Partial Yes Crypto only Yes Yes Perpetuals/crypto venues Forex Yes Outdated No No Yes No Yes Yes No AI agent runtime Built-in No FreqAI (ML) No No ML pipeline No No Scripts/controllers Broker execution Alpaca, IBKR, Tradier, Schwab, Tradovate, TopstepX (via ProjectX), Bitunix, Polymarket, selected CCXT IB only (outdated) Crypto exchanges None None Crypto exchanges No Exchange adapters Crypto exchanges Hosted deployment path BotSpot No No No No Paid cloud No No Hummingbot Foundation/enterprise ecosystem License GPL-3.0 GPL-3.0 GPL-3.0 Apache-2.0 AGPL-3.0 MIT Apache-2.0 LGPL-3.0 Apache-2.0 Switching from Backtrader? See our migration guide for a side-by-side comparison with code examples. Run your first AI backtest Start with SPY. A research agent analyzes its trend; a trading agent checks the evidence and account, then decides whether to buy, hold, or sell. This example limits a new position to 10% of the simulated portfolio. You need Python 3.10+ and an OpenAI API key ( OPENAI_API_KEY ). The default model is openai/gpt-6-luna on medium reasoning. Historical prices come from Yahoo; this backtest does not connect to a broker account. Model usage may incur charges. python -m pip install "git+https://github.com/Lumiwealth/lumibot.git@version/4.6.3" export OPENAI_API_KEY = "your-openai-api-key" export BACKTESTING_DATA_SOURCE = yahoo python -m lumibot.example_strategies.ai_researcher_trader The command runs a historical backtest of the complete SPY example. Watch the research and trading decisions, then inspect the generated simulated order records. Open its source. Customize the backtest Save as my_ai_strategy.py : from datetime import datetime from lumibot.backtesting import YahooDataBacktesting from lumibot.example_strategies.ai_researcher_trader import ResearcherTraderStrategy if __name__ == "__main__" : ResearcherTraderStrategy . backtest ( YahooDataBacktesting , datetime ( 2026 , 4 , 6 ), datetime ( 2026 , 4 , 11 ), budget = 100_000 , benchmark_asset = "SPY" , parameters = { "symbol" : "SPY" , "max_position_pct" : 10 }, ) python my_ai_strategy.py Watch the research and trading decisions in the log, then inspect the orders and backtest report. The $100,000 is simulated portfolio capital. The example uses openai/gpt-6-luna on medium reasoning. Open the complete strategy code to change the prompts, tools, or trading rules. Follow the walkthrough for the agent setup and how to read the results. Prefer rules without AI? Run a conventional Python strategy. Recorded run (made with Gemini, before GPT-6 Luna became the default): ten fresh agent runs across April 6 to 10, with one verified fill for 15 SPY shares. Inspect the source, decisions, and trade records. Fresh AI decisions can vary. If this helps you build, star LumiBot so you can find it again and share your strategy with the community. Explore an AI trading team The large-cap stock example has four agents: a researcher ranks stocks, a bull makes the case, a bear challenges it, and a trader decides what to do. See the code, run commands, and recorded results. A fresh model run can choose different trades and returns. Replaying saved decisions is different from asking the model to reason again. Want help building your first AI trading bot? Join the free challenge with Rob Grzesik, creator of LumiBot. Follow the training and learn how to turn an idea into an AI trading strategy. Looking for deeper training? Explore the AI Trading Bootcamp. Prefer a hosted workspace? Explore BotSpot. 🌐 Community Reddit Community Discord Community What You Can Build Deterministic strategies: normal Python logic, indicators, if statements, scheduled rules, position sizing, and risk controls. normal Python logic, indicators, if statements, scheduled rules, position sizing, and risk controls. AI-agent strategies: one or more agents that reason through evidence, call tools, write memory, and optionally place orders. one or more agents that reason through evidence, call tools, write memory, and optionally place orders. Backtests: replay historical data and simulated orders with artifacts you can inspect. replay historical data and simulated orders with artifacts you can inspect. Paper or live trading: reuse the same strategy code with real broker state and real order routing. Start with the open-source docs, then deploy when you are ready: Lumibot documentation · Try a sample Lumibot strategy on BotSpot Quick Start Choose a starting point: Run a Python backtest: the complete example below, using daily Yahoo data without a broker account. the complete example below, using daily Yahoo data without a broker account. Build an AI agent: AI Agents Quick Start, with installation, model credentials, and a complete backtest runner. AI Agents Quick Start, with installation, model credentials, and a complete backtest runner. Explore an options strategy: AI iron condor, including data requirements and the limits of the recorded evidence. Building a product on LumiBot? Partner with LumiBot through funded integrations, open-source maintenance, joint tutorials, or strategic collaboration. Backtest a strategy This is a traditional rules-based strategy: buy 10 AAPL shares on the first iteration and hold. It uses Yahoo historical prices, requires internet access, and makes no AI calls. Use Python 3.10 or later. pip install lumibot Save this as my_strategy.py : from datetime import datetime from lumibot.strategies import Strategy from lumibot.backtesting import YahooDataBacktesting class MyStrategy ( Strategy ): def on_trading_iteration ( self ): if self . first_iteration : aapl = self . create_order ( "AAPL" , 10 , "buy" ) self . submit_order ( aapl ) MyStrategy . backtest ( YahooDataBacktesting , datetime ( 2023 , 1 , 1 ), datetime ( 2024 , 1 , 1 ), ) python my_strategy.py Run the same strategy with a paper broker After the backtest works, keep the MyStrategy class and replace the final MyStrategy.backtest(...) call with a broker runner. This example uses Alpaca paper trading: export ALPACA_API_KEY = 'your-alpaca-key' export ALPACA_API_SECRET = 'your-alpaca-secret' export ALPACA_IS_PAPER = true python my_strategy.py import os from lumibot.brokers import Alpaca from lumibot.traders import Trader ALPACA_CONFIG = { "API_KEY" : os . environ [ "ALPACA_API_KEY" ], "API_SECRET" : os . environ [ "ALPACA_API_SECRET" ], "PAPER" : os . environ . get ( "ALPACA_IS_PAPER" , "true" ) . lower () != "false" , } broker = Alpaca ( ALPACA_CONFIG ) strategy = MyStrategy ( broker = broker ) trader = Trader () trader . add_strategy ( strategy ) trader . run_all () Start with paper trading. When you are ready for live trading, use the same strategy class and switch your broker account/configuration intentionally. For full setup guides, broker tutorials, AI-agent docs, examples, and deployment notes, use the Lumibot documentation. AI Trading Team Lumibot now includes a built-in AI agent runtime for financial research, reasoning, debate, risk review, and trade execution. Agents can inspect market data, read filings, query indicators, search memory, compare macro context, and submit orders through the same Lumibot strategy loop used by normal backtests and live trading. Classic Python strategies are still first-class. Lumibot lets you choose the right level of intelligence: fixed rules, AI agents, or a hybrid where Python handles the hard gates and agents reason through evidence. Built-in AI agent tools include market/account state, order inspection, DuckDB queries, documentation search, Alpaca news when credentials exist, technical indicators, SEC fundamentals and filings, FRED macro data, local memory, and Telegram notifications. Explore existing AI strategies Start with stock opening range breakout, large-cap stock teams, or macro research. Explore the public Macro Insight AI: Bridgewater-Style Strategy listing on BotSpot. Inspect their published code and available observations before using them. These educational implementations have no affiliation or endorsement from the named firms or people. BotSpot plans, model usage, broker access, and data requirements may apply. Design Your AI Trading Team An AI trading team is just a group of agents with different jobs inside the same Lumibot strategy. You can build a single-agent strategy, a specialist research flow, bull/bear/neutral teams, model-vs-model debates, deterministic execution gates, or agent reviewers layered on top of normal Python logic. Example: Research, Bull, Bear, and Trader Agents Here is one example pattern: a researcher gathers evidence, bull and bear agents debate the trade, and a trader agent decides what to buy or sell. In this pattern, each agent has a job: Research Agent: builds the evidence pack from market data, filings, fundamentals, news, macro data, and indicators. Bull Agent: turns that evidence into the strongest long thesis. Bear Agent: challenges the thesis, looks for risk, and argues for avoiding, delaying, or reducing the trade. Trader / Portfolio Manager Agent: checks cash, positions, open orders, and risk limits, then decides whether to trade. The copy-paste example below implements that exact team. It uses GPT-6 Luna on medium reasoning, LumiBot's default model. To run it with a broker in paper mode, set your AI and Alpaca credentials and run the file: export OPENAI_API_KEY = 'your-key-here' export ALPACA_API_KEY = 'your-alpaca-key' export ALPACA_API_SECRET = 'your-alpaca-secret' export ALPACA_IS_PAPER = true python ai_trading_team_bull_bear_leveraged_etf.py To backtest the same strategy instead, change IS_BACKTESTING = False to IS_BACKTESTING = True in the runner: export OPENAI_API_KEY = 'your-key-here' python ai_trading_team_bull_bear_leveraged_etf.py Save this as ai_trading_team_bull_bear_leveraged_etf.py . If an AI key is missing or invalid, Lumibot stops and prints a clear provider key error with a link to create a key. import os from datetime import datetime from lumibot.strategies.strategy import Strategy class AITradingTeamBullBearLeveragedETFStrategy ( Strategy ): parameters = { "universe" : [ "TQQQ" , "SQQQ" , "UPRO" , "SPXU" , "UDOW" , "SDOW" , "TNA" , "TZA" , "TECL" , "TECS" , "SOXL" , "SOXS" , "WEBL" , "WEBS" , "FAS" , "FAZ" , "LABU" , "LABD" , "ERX" , "ERY" , "GUSH" , "DRIP" , "DRN" , "DRV" , "TMF" , "TMV" , "NUGT" , "DUST" ], } def initialize ( self ): self . sleeptime = "1D" model = os . environ . get ( "AI_TRADING_TEAM_MODEL" , "openai/gpt-6-luna" ) # The first three agents are read-only. They can reason, but cannot trade. self . agents . create ( name = "researcher" , model = model , allow_trading = False , system_prompt = "Rank the ETFs by upside. Be direct." , ) self . agents . create ( name = "bull" , model = model , allow_trading = False , system_prompt = "Argue for the strongest money-making trade." , ) self . agents . create ( name = "bear" , model = model , allow_trading = False , system_prompt = "Point out the biggest risk, briefly." , ) # Only this final agent can submit orders through Lumibot. self . agents . create ( name = "trader" , model = model , allow_trading = True , system_prompt = "Buy one ETF from the universe aggressively. Use nearly all cash." , ) def on_trading_iteration ( self ): # Each trading day, pass the same market context through the team. context = { "date" : self . get_datetime () . date () . isoformat (), "universe" : self . parameters [ "universe" ], } research = self . agents [ "researcher" ] . run ( task_prompt = "Pick the strongest ETF." , context = context , ) bull = self . agents [ "bull" ] . run ( task_prompt = "Make the bull case." , context = { ** context , "research" : research . summary }, ) bear = self . agents [ "bear" ] . run ( task_prompt = "Make the bear case." , context = { ** context , "research" : research . summary , "bull" : bull . summary }, ) self . agents [ "trader" ] . run ( task_prompt = "Sell anything that is not the pick, then buy the best ETF with nearly all available cash." , context = { ** context , "research" : research . summary , "bull" : bull . summary , "bear" : bear . summary }, ) if __name__ == "__main__" : IS_BACKTESTING = False if IS_BACKTESTING : from lumibot.backtesting import YahooDataBacktesting AITradingTeamBullBearLeveragedETFStrategy . backtest ( YahooDataBacktesting , datetime ( 2026 , 4 , 7 ), datetime ( 2026 , 5 , 22 ), ) else : from lumibot.brokers import Alpaca from lumibot.traders import Trader ALPACA_CONFIG = { "API_KEY" : os . environ [ "ALPACA_API_KEY" ], "API_SECRET" : os . environ [ "ALPACA_API_SECRET" ], "PAPER" : os . environ . get ( "ALPACA_IS_PAPER" , "true" ) . lower () != "false" , } broker = Alpaca ( ALPACA_CONFIG ) strategy = AITradingTeamBullBearLeveragedETFStrategy ( broker = broker ) trader = Trader () trader . add_strategy ( strategy ) trader . run_all () Backtests are not expected future performance. The point is that the full AI trading team runs inside Lumibot's normal broker and backtest loops, so the decisions, orders, and artifacts are inspectable before you connect real money. More AI Trading Team Examples These examples show different ways to organize an AI trading team. Each page explains the inspiration, the agent flow, how to run it with a broker in paper mode, and how to backtest it. Run Lumibot Without Managing Servers BotSpot is the managed cloud built around Lumibot. It makes Lumibot easier and cheaper to run because the data, backtesting workers, broker connections, scheduling, monitoring, logs, alerts, and kill switches are already wired together. BotSpot is not a generic chatbot bolted onto a broker account. Its AI workflows, prompts, MCP tools, backtest setup, broker paths, and deployment flow are built for Lumibot. Backtesting data included. Use hosted stock, futures, options, FRED macro, SEC filing, and other supported data without wrangling every feed and API key yourself. Some data is included; premium data can be much cheaper than buying direct subscriptions for occasional experiments. Use hosted stock, futures, options, FRED macro, SEC filing, and other supported data without wrangling every feed and API key yourself. Some data is included; premium data can be much cheaper than buying direct subscriptions for occasional experiments. Cheaper deployment at scale. Scheduled and periodic bots should not need a full always-on server per strategy. BotSpot runs Lumibot bots on managed infrastructure built for this workflow, with monitoring and controls included. Scheduled and periodic bots should not need a full always-on server per strategy. BotSpot runs Lumibot bots on managed infrastructure built for this workflow, with monitoring and controls included. Lumibot-tuned AI. Generic coding tools can write Python, but BotSpot is tuned for Lumibot strategy structure, broker setup, backtests, artifacts, and deployment. Generic coding tools can write Python, but BotSpot is tuned for Lumibot strategy structure, broker setup, backtests, artifacts, and deployment. MCP for coding agents. Connect BotSpot to Codex, Claude Code, Cursor, and other MCP clients so your coding agent can run backtests, inspect artifacts, compare results, and prepare deployment instead of only generating code. Connect BotSpot to Codex, Claude Code, Cursor, and other MCP clients so your coding agent can run backtests, inspect artifacts, compare results, and prepare deployment instead of only generating code. Work from anywhere. Use the same strategy workspace from the web, your phone, Telegram, Discord, Claude, ChatGPT, and coding tools. Start in one place and continue in another. Use the same strategy workspace from the web, your phone, Telegram, Discord, Claude, ChatGPT, and coding tools. Start in one place and continue in another. Marketplace and strategy library. Browse existing strategy code, clone and adapt strategies, run strategies where available, and publish your own strategies when you are ready. Browse existing strategy code, clone and adapt strategies, run strategies where available, and publish your own strategies when you are ready. Observability and control. Inspect why a bot bought or sold, review charts, logs, decisions, orders, notifications, audit history, and kill-switch controls in one place. Deploy Live Option A: BotSpot (managed cloud) BotSpot is the managed path for taking a Lumibot strategy from idea to backtest to paper or live trading. It handles the expensive and fragile parts around the strategy: hosted data setup for supported backtests, parallel backtest runs, broker connections, scheduling, logs, alerts, monitoring, audit history, and kill-switch controls. This is especially useful when your strategy only needs to run daily or periodically. You get the same Lumibot code path without paying for always-on infrastructure, maintaining a scheduler, hand-wiring broker secrets, or building your own log and alerting stack. Option B: Self-hosted (full control) Run Lumibot on your own machine with any supported broker: from lumibot.brokers import Alpaca from lumibot.traders import Trader ALPACA_CONFIG = { "API_KEY" : "your-key" , "API_SECRET" : "your-secret" , "PAPER" : True , } broker = Alpaca ( ALPACA_CONFIG ) strategy = MyStrategy ( broker = broker ) trader = Trader () trader . add_strategy ( strategy ) trader . run_all () Supported Brokers Lumibot supports stocks, options, crypto, futures, forex, indexes, and prediction contracts across several broker integrations: Alpaca Interactive Brokers and Interactive Brokers REST Tradier Schwab Tradovate TopstepX futures (via ProjectX) Bitunix Polymarket prediction-contract trading and backtesting Selected CCXT crypto paths. Coinbase, Kraken, and WEEX have auto-detected credential paths; KuCoin, Binance, and BitMEX have documented manual CCXT setup paths; Kraken, Binance, KuCoin, BitMEX, Bybit, and OKX have documented backtesting examples. Lumibot does not claim blanket support for every CCXT exchange. Select Backtesting Data Sources Lumibot can backtest from free daily data, broker data, premium market data, and your own files: Yahoo Finance Alpaca Interactive Brokers REST ThetaData Polygon/Massive DataBento Tradier Schwab Polymarket prediction-contract price history CCXT backtesting examples: Kraken, Binance, KuCoin, BitMEX, Bybit, and OKX Pandas/CSV dataframes Recommended Data Provider For the deepest historical coverage (stocks, options, futures, indexes), we recommend ThetaData. Use promo code BotSpot10 for 10% off your first order. AI Trading Agents Lumibot includes a built-in AI trading agent runtime. Build agents that run identically in backtests and live trading. Create agents with self.agents.create(...) The default model is openai/gpt-6-luna on medium reasoning; set model= per agent to use any other LiteLLM/ADK-supported provider string on medium reasoning; set per agent to use any other LiteLLM/ADK-supported provider string Make research agents read-only with allow_trading=False Give agents built-in SEC fundamentals, filings, FRED macro data, indicators, memory, and notifications Use DuckDB for time-series analysis instead of dumping raw bars into prompts for time-series analysis instead of dumping raw bars into prompts Mount external MCP servers for news, macro data, filings, or any domain-specific tools for news, macro data, filings, or any domain-specific tools Replay identical agent decisions in backtests without paying for another model call Use BotSpot MCP when you want an AI coding agent to generate Lumibot strategies, launch backtests, inspect artifacts, and iterate without leaving your editor. Start here: Memory and Traceability AI strategies can record proposals, risk notes, actual trading decisions, submitted orders, lessons, open theses, tool calls, and run artifacts as local SQLite and Parquet files. This makes an AI backtest reviewable instead of a black box: you can inspect why the agent traded, which tools it used, what memory it retrieved, and what it remembered for later iterations. Memory events include agent/model-call provenance when they come from agent tools. Community Strategies Browse and contribute open-source strategies: lumibot-strategies. For hosted strategy discovery with performance, descriptions, visuals, and deploy flows, use the BotSpot marketplace. Example Strategies Lumibot includes 25+ example strategies covering stocks, options, crypto, futures, forex, and Polymarket prediction contracts: # Run a simple buy-and-hold backtest python -m lumibot.example_strategies.stock_buy_and_hold # Or explore all examples ls lumibot/example_strategies/ Browse all examples: example_strategies/ Polymarket example: polymarket_prediction_contract.py External example repo: stock_example_algo shows a minimal strategy repository you can run yourself or adapt inside BotSpot. Backtesting Data Sources Select a data source via environment variable (overrides code): export BACKTESTING_DATA_SOURCE = thetadata # or yahoo, ibkr, polygon, polymarket Multi-provider routing by asset type: export BACKTESTING_DATA_SOURCE = '{"default":"thetadata","option":"thetadata","crypto":"ibkr","crypto_future":"ibkr","future":"ibkr","cont_future":"ibkr"}' Crypto futures/perpetual backtests can route Asset.AssetType.CRYPTO_FUTURE through spot crypto history. Quote symbols are preserved: BTCUSDT uses BTC/USDT spot history when that pair is available from the selected crypto data source. LumiBot must not silently replace a requested USDT, USDC, EUR, or other quote with USD; missing pairs should fail honestly or be changed explicitly in strategy code. Data source comparison Data Source OHLCV Split Adjusted Dividends Dividend Adjusted Returns Yahoo Yes Yes Yes Yes Alpaca Yes Yes No No Polygon Yes Yes No No Tradier Yes Yes No No Polymarket Yes N/A N/A N/A Pandas* Yes Yes Yes Yes *Pandas loads CSV files in Yahoo dataframe format, which can contain dividends. Learn More Project Growth AI Bootcamp Learn to build, backtest, and deploy trading strategies using AI. Join 2,400+ traders. Contributing We welcome contributions! Here's a video to help you get started: Watch The Video Steps: Clone the repository Create a new branch: git switch -c my-feature Install dev dependencies: pip install -r requirements_dev.txt && pip install -e . Make your changes Run tests: pytest Create a pull request Running Tests pytest # Run all tests pytest tests/test_asset.py # Run a specific test file coverage run ; coverage report # Show code coverage Remote Cache Configuration Lumibot can mirror its local parquet caches to AWS S3. See docs/remote_cache.md for configuration. Architecture Documentation Disclaimer This software is provided for educational and informational purposes only. It is not financial advice and does not constitute a recommendation to buy or sell any security. Lumibot and BotSpot are not registered broker-dealers or financial advisors. Algorithmic trading involves substantial risk of loss, including the possibility of losses greater than your initial investment. Software bugs and errors can lead to rapid financial losses. Past backtest performance does not guarantee future results. Use this software at your own risk. You are solely responsible for compliance with all applicable laws and regulations regarding the assets you choose to trade. Affiliate disclosure: some provider links or promo codes, including ThetaData, may support continued Lumibot development. License GNU General Public License v3.0 - View License Contribute See CONTRIBUTING.md for reproducible bugs, examples, tests, and focused pull requests. If LumiBot is useful to your work, star the repository and share an example with another developer.

Standard Chartered (STAN) plans to offer custody in Singapore for selected cryptocurrencies, stablecoins and tokenized real-world assets, expanding a digital asset business that already reaches financial hubs including the United Arab Emirates (UAE), Luxembourg …

Standard Chartered (STAN) plans to offer custody in Singapore for selected cryptocurrencies, stablecoins and tokenized real-world assets, expanding a digital asset business that already reaches financial hubs including the United Arab Emirates (UAE), Luxembourg and Hong Kong. The service, which remains subject to applicable regulatory requirements, would be available to institutional clients and accredited investor corporate clients, the bank said Thursday. It would sit within Standard Chartered’s financing and securities-services business rather than operate as a retail crypto product. “As adoption grows, we are seeing institutional use cases and client interest emerge around tokenised funds, ETFs and precious metals, as clients look for more efficient ways to hold, transfer and mobilise assets,” Ying Ying Tan, global head of digital assets and securities services at Standard Chartered told CoinDesk via email. “Our Singapore launch further strengthens our ability to help clients bridge traditional and digital markets through secure, regulated and bank-grade infrastructure,” she added. The move is not a first for a global bank. BNY, for example, expanded its digital-asset custody platform to include USDC custody and minting, and later added staking.

A pledge of at least €13 billion for AI infrastructure is pulling new data center projects into Finland, though regulators have already halted work at two sites Google has picked Finland for the biggest …

A pledge of at least €13 billion for AI infrastructure is pulling new data center projects into Finland, though regulators have already halted work at two sites Google has picked Finland for the biggest single bet it has ever made in Europe. The company announced on September 9, 2026, that it will invest at least €13 billion ($15.1 billion) in AI infrastructure and data centers across the country. There is one complication. A month after the announcement, Finnish regulators told Google to stop work at two of its new sites. Where the €13 billion is going The spending is scheduled to land over roughly two years, covering 2027 to 2028. It will fund three entirely new data centers in Kajaani, Muhos, and Vaala. Google will also expand its existing campus in Hamina, which has been running since 2009. For scale, Google had already put €4.5 billion into Hamina before this announcement. The new pledge is nearly three times that figure, and it is described as a floor rather than a ceiling. The jobs projections are hefty. The investment is expected to support more than 37,000 jobs during the construction phase, with approximately 16,000 of those directly in construction work. Advertisement Once the facilities are operational, the project is expected to sustain around 7,000 ongoing jobs. Those positions are projected to pay wages approximately 24% above the Finnish median. The investment is forecast to add an average of €3.6 billion a year to Finland’s GDP. AI, tech, and the markets they move—in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Email address Subscribe free We respect your privacy. Unsubscribe anytime. Powering the machines Google signed a 22-year power purchase agreement with utility Fortum that lets it buy up to 50% of the output from the Loviisa nuclear plant. The arrangement is also expected to support grid stability. Google has also lined up contracts for wind energy and a 94 MW battery system, which can store power and release it when demand spikes or the wind drops off. Why Finland keeps winning these deals Finland’s pitch to data center operators comes down to two things: the cold and the grid. Low temperatures sharply cut the cost of keeping servers from overheating, which is one of the largest ongoing expenses in running a facility. The country also offers access to cleaner energy, including nuclear and hydroelectric power. The regulators hit pause The rollout hit a snag on October 6, 2026. Finland’s Licensing and Supervisory Agency (LVV) ordered work to stop at the Muhos and Kajaani sites. The issue was timing. Forest had been cleared at the sites before environmental impact assessments were finished. Google acknowledged it had fallen short of compliance. The company pledged to follow the rules going forward and has outlined biodiversity measures, including planting 130 hectares of trees at the Muhos site where work was halted. What this means For Finland, the stakes are mostly economic, and they are large. A projected €3.6 billion annual GDP contribution and thousands of above-median-wage jobs would reshape regions like Kajaani, Muhos, and Vaala. Energy is the other pressure point. Committing up to half of Loviisa’s output to one customer for 22 years is a major structural shift for Finland’s power market.

This article examines how recent events may relate to prediction market pricing. It reflects interpretive analysis of publicly available information and is provided for informational purposes only. Observers will be monitoring upcoming economic indicators, …

This article examines how recent events may relate to prediction market pricing. It reflects interpretive analysis of publicly available information and is provided for informational purposes only. Observers will be monitoring upcoming economic indicators, such as inflation and wage growth data, ahead of the MPC’s November meeting. Indications from other MPC members, including Governor Andrew Bailey, could provide further insights into the Bank’s policy direction. Any shifts in energy prices or geopolitical developments may also influence market expectations and the Bank’s approach to interest rates. Bank of England Chief Economist Huw Pill has emphasized the need for monetary policy to prioritize controlling inflation. Pill’s remarks come as UK inflation remains above the target at 3.1%, with the Bank Rate at 3.75%. His comments suggest a cautious approach to monetary policy amid concerns about persistent inflationary pressures from rising energy costs and geopolitical tensions. While Pill’s statement does not confirm an imminent decision by the Monetary Policy Committee (MPC) to raise rates, it indicates a continued focus on inflation management. Disclaimer This article contains analysis of publicly available information and market data and is for informational purposes only. It does not constitute investment advice or a recommendation to buy, sell, or hold any asset or contract. Content may include AI-assisted interpretation and may be incomplete or subject to change. Market conditions may evolve rapidly, and the timing of information may affect how it is interpreted. Market participants may act on similar information at or around the time it becomes available. You are solely responsible for any decisions made based on this content. For additional details, please review our full Disclaimer & Risk Disclosure.

Italian companies expect further increases in inflation over the next 12 months, following the spikes linked to the Iran war, due to hikes in raw material costs, according to a survey of industrial and …

Italian companies expect further increases in inflation over the next 12 months, following the spikes linked to the Iran war, due to hikes in raw material costs, according to a survey of industrial and service firms released on Thursday by the Bank of Italy. … Se hai scelto di non accettare i cookie di profilazione e tracciamento, puoi aderire all’abbonamento "Consentless" a un costo molto accessibile, oppure scegliere un altro abbonamento per accedere ad …

State Bank of India Chairman C S Setty on Thursday said he does not see deposit rate hike for up to three months because of the excess liquidity in the system. Speaking a day …

State Bank of India Chairman C S Setty on Thursday said he does not see deposit rate hike for up to three months because of the excess liquidity in the system. Speaking a day after the RBI's policy announcement, Setty said the shift to tightening of rates by the central bank will help banks expand their net interest margins (NIMs) for up to three quarters. "I believe that next two-three months, there may not be any rate action on the deposits because we have sufficient liquidity in the system," Setty told reporters here. He, however, added that if the credit growth continues at the ongoing elevated levels, some banks may have to look at raising deposit rates to fund the advances. Setty also acknowledged that depositors need to be compensated with some level of positive real interest rate in a scenario where inflation is inching up. On the credit growth, he said the country's largest lender will be able to sustain its loan book expansion at a rate of 14-15 per cent. "While there is no ideal credit growth rate, at least in SBI, we believe that you have to be 2-3 per cent more than the nominal GDP. If you are looking at a nominal GDP of 12 per cent, 14-15 per cent growth rate (in credit) is something that will sustain the momentum of the growth," he explained. Setty, who also chairs the industry grouping Indian Banks Association, dismissed fears of an imprudence in lending by the banks because of the nearly USD 133 billion deposit raise from the diaspora. He said the FCNR(B)-related liquidity will get consumed in the next 2-3 quarters. "And in the interim, when you have spike in the liquidity, the RBI is also taking measures to absorb the liquidity. I think this combination of absorption activity of RBI and the requirement of credit growth would enable us that there is no exuberance or imprudence on lending," he added. Asked if the RBI's rate hike of Wednesday, and SBI's house view of a 0.50 per cent more will help net interest margins, he replied in the positive but refrained from giving any levels citing silent period before the earnings announcement. "...in the next 2-3 quarters, it (RBI rate hikes) is positive on the NIMS. People are expecting that the 75 bps will happen in 2 or 3 hikes. But whatever happens, I think this benefit (on NIMs) is available for 2-3 quarters," he said. Setty added that over 50 per cent of the loans in the banking system are tied to the external benchmark-based lending rate, which get repriced as per RBI's actions on the repo rate. On the five-day work week demand over which bank unions had also threatened to go on strike recently, Setty said bank managements are engaged with all stakeholders on the issue through a committee formed under IBA, and it is premature to comment on the subject. Meanwhile, Setty said making banking simpler, relevant, personalised and more safe will be the key focus areas in the next stage of financial inclusion efforts. Stating that India is among the few countries where over 95 per cent of the population has a bank account, he said we also need to devote more attention to making these accounts more operational going forward. He also urged foreign investors to look beyond the current size of the Indian economy and factor in the transformation path it is on while making their decisions.

Ethereum enters Q4 2026 with a major protocol catalyst, stronger market momentum, and a significant recovery in the first half. These factors combined could help Ethereum to finally close some of its distance to …

Ethereum enters Q4 2026 with a major protocol catalyst, stronger market momentum, and a significant recovery in the first half. These factors combined could help Ethereum to finally close some of its distance to Bitcoin. Read More: He Says His Binary Options Strategy Brings In $2,000 a Week—Watch Him Trade Live What Is Ethereum’s Glamsterdam Upgrade? The Ethereum Glamsterdam upgrade is the network’s next major hard fork after Fusaka. It combines execution and consensus layer changes to scale layer 1, as well as improve the practicality of validation for independent node operators. Related: Top 10 Undervalued Cryptocurrencies That Could Outperform Bitcoin in Q4 2026 When Will Glamsterdam Launch on Ethereum Mainnet? The Ethereum Glamsterdam launch date remains unconfirmed, but developers expect it in Q4 2026. Sepolia testing has begun in early October. As with all major upgrades, the Ethereum Glamsterdam activation date depends on testnet stability, client readiness, and security reviews. Why Glamsterdam Matters for Ethereum’s Next Growth Phase Ethereum has been relying on layer-2 and cheaper data availability solutions to scale for years. Glamsterdam brings more attention back to layer-1 execution and capacity, while layer-2 adoption will continue to grow alongside it. How Glamsterdam Builds on the Fusaka Upgrade The Ethereum Fusaka upgrade was primarily focused on data availability and supporting Ethereum’s layer-2 infrastructure. Glamsterdam picks up where Fusaka left off and focuses more on execution and validation changes, creating a clearer path to higher throughput. LATEST: ⚡️ Ethereum's Glamsterdam upgrade has gone live on the Sepolia testnet. pic.twitter.com/cbHzLnlHUp — CoinMarketCap (@CoinMarketCap) October 6, 2026 What Will the Glamsterdam Upgrade Change? The Ethereum Glamsterdam changes block production, transaction processing, gas accounting, and node synchronization. They are all focused on increasing throughput, without compromising the practicality of Ethereum validation. Enshrined Proposer-Builder Separation (ePBS) Enshrined proposer-builder separation moves an important part of block production directly into Ethereum’s protocol. It reduces some of the trust assumptions and allows for larger payloads to be propagated with enough time for processing. Block-Level Access Lists and Parallel Processing Block-level access lists (BALs) indicate which accounts and storage locations will be accessed and modified by a block. It allows validators to process some operations in parallel and prepare disk reads. BALs can allow Ethereum to utilize parallel processing capabilities and remove an important bottleneck in execution. Higher Ethereum Network Capacity Glamsterdam creates the necessary infrastructure for higher capacity in the future, without immediately increasing throughput. Using longer propagation windows and faster state access, Ethereum can eventually increase the gas limit and utilize more of the bandwidth for data. Validators receive more information and time to process individual blocks, opening up capacity for more transactions. Lower Transaction Costs One of the Ethereum upgrade 2026 changes is related to transaction gas costs. Intrinsic gas costs will be changed in a way that makes basic transactions cheaper. Increased capacity can also reduce some of the congestion, while the general Ethereum gas price in 2026 will still depend on demand. Related: Best Memecoins to Buy in October 2026: 10 Tokens Ready for the Next Rally More Efficient Data Handling and Node Syncing Block-level access lists contain information about data access and final state modifications. It allows nodes to utilize the information for validation, instead of processing each operation from scratch. It reduces the amount of processing required for synchronization and can greatly improve the efficiency of the Ethereum infrastructure at scale. Area Glamsterdam Change Why It Matters for Ethereum Potential Impact on ETH ▲ $2,518.27 Block Production Enshrined Proposer-Builder Separation (ePBS) Moves more block-building logic into Ethereum’s protocol and reduces reliance on external middleware Could improve network efficiency and strengthen Ethereum’s infrastructure narrative Transaction Processing Block-Level Access Lists Helps validators identify accessed accounts and storage before full execution Supports parallel processing and higher future throughput Network Capacity Higher Layer-1 Capacity Creates room for larger gas limits and more activity on Ethereum mainnet More usage could strengthen demand for ETH Transaction Costs Lower Intrinsic Gas Costs Makes basic transactions cheaper and reduces some user friction Lower fees could attract more activity, but may reduce ETH burn Node Infrastructure More Efficient Syncing Reduces unnecessary processing and improves state synchronization Helps Ethereum scale without excessive hardware requirements Layer-2 Support Better Base-Layer Efficiency Gives rollups stronger settlement and data infrastructure Could reinforce Ethereum’s position as the dominant Layer-2 settlement layer DeFi Greater Capacity and Lower Costs Makes trading, lending, and other DeFi activity more efficient Higher DeFi usage could support ETH demand Tokenization Improved Institutional Infrastructure Strengthens Ethereum’s ability to support tokenized assets and financial products Could attract more institutional activity ETFs Renewed Ethereum ETF Inflows Adds a regulated source of demand outside crypto-native markets Strong inflows could help ETH recover against Bitcoin ETH vs Bitcoin Potential Relative Revaluation ETH still trades well below earlier ETH/ BTC ▲ $77,666.00 cycle levels A successful upgrade and stronger inflows could support ETH outperformance Main Risk Upgrade Delays Complex protocol changes may require additional testing Delays could weaken short-term Q4 momentum Competitive Risk Solana and Other Layer-1s Rival networks continue competing on speed, costs, and user growth Ethereum must convert technical improvements into real activity Лучшее место — после H2 “What Will the Glamsterdam Upgrade Change?” и перед H3 “Enshrined Proposer-Builder Separation (ePBS)”. Why Glamsterdam Could Be a Major Catalyst for ETH Glamsterdam directly addresses many of the scalability and infrastructure-related concerns affecting Ethereum’s long-term competitiveness. Faster and More Scalable Ethereum Infrastructure Higher throughput can make Ethereum infrastructure more attractive for applications requiring higher settlement capacity. Developers have more breathing room and are less reliant on external scaling solutions. End-users also benefit from the increased capacity during peak seasons. Rising utilization alongside increased capacity would strengthen Ethereum’s price case for 2026. Lower Fees Could Drive More Network Activity High fees act as a disincentive for many users and channel transactions into cheaper chains. Ethereum’s move towards lower fees could recapture some of the value and drive more network activity across different applications. Simplified transactions open the door to a broader audience of consumers and institutions. Ethereum gas fees in 2026 will still be driven by demand, but cheaper base layer transactions are a significant advantage. Better Support for Ethereum Layer-2 Networks Glamsterdam supports Ethereum’s layer-2 strategy by creating the infrastructure necessary for higher capacity. More efficient block production also allows for larger effective payload sizes and continued data scaling. Layer-2 networks rely on Ethereum for settlement, security, and data availability. Stronger Position in DeFi and Tokenization Ethereum plays a critical role in the DeFi, stablecoin, and tokenization ecosystems. Better scalability can reinforce Ethereum’s position as the preferred platform for yield generation and institutional treasury management. Tokenization requires reliable infrastructure, deep liquidity, and predictable settlement windows – all of which Ethereum already possesses, but can further improve upon with higher capacity. Related: Best Crypto to Buy Before the Next Crash: Top 5 Coins to Watch Through the End of 2026 Ethereum vs. Bitcoin: Can ETH Finally Catch Up? The ETH vs. Bitcoin debate has become more interesting following Ethereum’s strong Q3 performance. ETH caught up with Bitcoin, but is still far from overtaking it over a longer period of time. How ETH Has Performed Against BTC in 2026 Ethereum struggled to keep up with Bitcoin for much of the cycle. It made less sense to allocate capital to ETH, due to its inferior liquidity profile, institutional adoption, and simpler investment narrative. This dynamic has changed in Q3, as ETH has been recovering faster than BTC. Even in the wake of the rally, the ETH/BTC ratio remains far from the previous cycle highs. Ethereum’s Q3 Rally vs. Bitcoin’s Performance Ethereum’s large-cap rally peaked in Q3 with a multi-year rise of over 70%. Bitcoin was also bullish, but it was a smaller gain of over 40%. ETH managed to recover much of its relative losses against BTC and demonstrate that the ETH vs Bitcoin 2026 debate can be quickly turned around during large-scale crypto-spring. Why ETH Still Trades at a Significant Discount to Its Potential Ethereum supports the largest DeFi, stablecoin, tokenization, and layer-2 ecosystems, but it still trades at a large discount to its previous dollar highs. ETH bears argue that competition, fragmented layer-2 activity, and insufficient fee capture hurt Ethereum’s value capture ability. They will be proven wrong if the usage growth outpaces these concerns. What Could Trigger an ETH/BTC Reversal? A reversal of the ETH BTC ratio would most likely be driven by multiple different catalysts. The Glamsterdam upgrade is one of them, but strong ETF inflows and DeFi usage growth can accelerate the process. The rise of tokenization could provide another powerful tailwind for Ethereum, while reduced Bitcoin dominance would facilitate the transition. Ethereum ETF Inflows Add Another Bullish Catalyst Ethereum ETF inflows create an important demand channel for ETH, separate from the protocol activity and network utility. They represent renewed institutional participation and can complement the technical improvements from Glamsterdam. Ethereum ETF Demand Is Returning There was a clear increase in demand for spot Ethereum ETFs, following a period of weakness early in the cycle. The change in Ethereum ETF inflows indicates that institutional investors are re-evaluating their positions in ETH. Spot Ethereum ETFs provide easier access to ETH, diversification benefits, and regulatory convenience. Strong and consistent Ethereum ETF inflows would have a bigger impact than individual strong days for the price. Institutional Interest in ETH vs. Bitcoin Bitcoin dominates the institutional crypto space, due to its superior size, liquidity, and ETF availability. Ethereum represents a different type of institutional exposure to crypto, focused on programmable money and finance. ETH gives institutional investors exposure to DeFi, tokenization, and settlement infrastructure. Could ETF Flows Accelerate the ETH/BTC Recovery? Strong Ethereum ETF inflows can accelerate the ETH/BTC ratio recovery, simply by creating sustained demand for the asset. Relative inflows matter more than the absolute size of Ethereum ETF inflows. Rapid growth of Ethereum products can drive the institutional positioning in favor of Ethereum. Weak ETF inflows would limit the potential for Q4 outperformance. What Could Glamsterdam Mean for ETH Price? Any ETH price prediction for 2026 should reflect the fundamental improvements, without overstating their impact on the price. While Glamsterdam creates a strong technical foundation for ETH’s growth, increased liquidity and market sentiment will remain critical. The Bull Case for Ethereum in Q4 2026 The bullish scenario for Ethereum in Q4 incorporates the successful execution of the Glamstand upgrade, strong ETF inflows, and increased network activity. A strong macro environment would complement these factors and set the conditions for a large-cap bull run. Ethereum has the potential to benefit from a rotation into large-cap altcoins, following Bitcoin’s recent strength. A smooth upgrade would reduce technical uncertainty at the end of the year. Related: Best Altcoins to Buy in October 2026 Before the Next Crypto Rally The Key ETH Price Levels to Watch ETH enters October around the $2,700 area before renewed market weakness. That makes the $2,600 to $2,700 region important for near-term momentum. A move above $3,000 would strengthen the bulls’ case. Failure to hold the mid-$2,000 area would derail the Q4 setup for Ethereum. Could ETH Outperform Bitcoin Before the End of 2026? ETH can outperform Bitcoin during Q4, without necessarily reaching new highs. The relative performance of ETH and BTC is determined by their respective returns, rather than absolute price levels. Ethereum has already demonstrated stronger momentum in Q3 and could extend its lead if the inflows and upgrades continue to contribute to the bullish narrative. The Biggest Risks to the Ethereum Glamsterdam Narrative Ethereum’s Glamsterdam upgrade creates a strong technical narrative, but there are multiple different catalysts that could hurt its reception. Investors should not mistake roadmap progress for price appreciation potential. The Upgrade Could Face Delays Protocol upgrades require extensive testing and coordination between different clients. Ethereum Glamsterdam makes some significant protocol changes that could see unexpected issues during testing. ePBS has already required substantial engineering work before its Ethereum deployment. Similar delays could occur during the testnet phase and offset some of the upgrade’s benefits. Ethereum Still Faces Competition From Solana and Other Layer-1s Solana continues to compete directly with Ethereum for trading volume, payments, consumer applications, and tokenization use cases. Other layer-1 networks also target many of Ethereum’s potential users. Ethereum has clear advantages in security and liquidity, but end-users frequently choose faster and cheaper alternatives. Lower Fees Could Reduce ETH Burn and Fee Revenue The lower transaction fees will directly offset some of the revenue and activity from Ethereum’s network. While cheaper transactions are beneficial for users, they reduce the value capture for ETH holders. The network will need to see increased activity to offset the reduced value capture per transaction. Macro Conditions Could Override Ethereum-Specific Catalysts Crypto remains highly sensitive to interest rates, liquidity, geopolitical risk, and other external factors. Strong fundamentals cannot fully offset a broad market downturn. Rising yields could hurt the value of both ETH and Bitcoin. The best Ethereum upgrade Q4 2026 scenario does not incorporate a market crash. What Comes After Glamsterdam for Ethereum? Glamsterdam represents one step in Ethereum’s long roadmap to much higher throughput and better transaction inclusion. Developers continue working on multiple approaches to increase capacity and improve the user experience at scale. Ethereum’s Long-Term Scaling Roadmap Ethereum’s long-term strategy incorporates both layer-1 and layer-2 approaches to scaling. Ethereum developers continue to work on execution improvements, as well as data availability and validator efficiency. The Ethereum tokenomics upgrades roadmap also emphasizes better inclusion guarantees, execution-focused account abstraction, and improved settlement finality. Glamsterdam lays the groundwork for these future upgrades. The Path Toward Higher L1 Throughput Ethereum will eventually need to significantly increase its layer-1 capacity to accommodate growing demand. Increasing throughput requires faster validation and state handling, which ePBS and BALs can facilitate. Longer propagation windows, better state management, and parallel processing capabilities can allow Ethereum to eventually increase its gas limit. Ethereum’s Role in the Future of DeFi and Tokenization Ethereum’s settlement layer role is critical for many DeFi protocols. Tokenization of real-world assets will also rely on Ethereum settlement and security. A stronger Layer-1 can support the needs of these expanding ecosystems and make Ethereum more attractive for institutional settlement and custody.

Gate has announced a collaboration with Visa to launch a crypto-linked card, further expanding the use of digital assets in everyday spending and global commerce. The product is expected to launch across 40+ markets …

Gate has announced a collaboration with Visa to launch a crypto-linked card, further expanding the use of digital assets in everyday spending and global commerce. The product is expected to launch across 40+ markets globally, allowing users to make payments with digital assets online and in-store wherever Visa is accepted and more conveniently connect their Gate accounts with real-world spending. When users make payments with the crypto-linked Visa card launched by Gate, the relevant crypto assets will be automatically converted into fiat currency at the point of sale, allowing digital assets held in their Gate accounts to be used for everyday shopping, dining, travel, and other expenses. Users can also apply for and manage the card through the Gate app and access related payment services within a familiar platform environment. Details regarding supported markets, eligibility requirements, supported assets, and service availability can be found on the product page. Visa has an extensive global digital payments acceptance network spanning more than 175 million merchant locations across more than 200 countries and territories. Through this partnership, Gate is connecting its digital asset capabilities with Visa’s payments infrastructure, enabling users to use digital assets across a broader range of commercial scenarios and further expanding their application in everyday payments. More than 160 stablecoin-linked Visa card programmes are now live globally, with payment volume up nearly 200% year-over-year, demonstrating the rapid expansion of crypto card adoption. This partnership builds on Gate’s existing global user base and multi-asset service capabilities. Founded in 2013, Gate now serves more than 61 million users, supports trading across 5,300+ crypto assets and 12,800+ stock assets, and provides access to TradFi products spanning metals, stocks, indices, forex, and commodities. The platform was among the first to implement 100% Proof of Reserves and has built a diversified product and ecosystem portfolio. Its broad user base, multi-asset product offering, and platform capabilities provide the foundation for Gate to extend the use of digital assets into everyday payment scenarios. Gate has officially launched Gate Money, a globally integrated financial services application. Gate Money brings digital assets, fiat currencies, stocks, ETFs, gold, bank accounts, and payments together in the Gate app. Eligible users can also apply online for a global bank account held in their own name and manage fund receipts, transfers, asset conversions, and payments in one place. Gate’s crypto-linked Visa card further connects assets held in Gate accounts with global payment scenarios, extending Gate Money’s financial services into more everyday spending use cases and giving users greater flexibility in how they use their assets. Currently, users can update the Gate App to v8.39.0 to enable Gate Money. Gate Founder and CEO, Dr. Han, said: “Digital assets are gradually becoming part of everyday financial life. This partnership with Visa connects the digital asset accounts Gate users already know with a broad range of commercial scenarios, making digital asset payments more intuitive and better aligned with everyday needs. As the service launches across more than 40 countries and territories, we aim to give users greater flexibility in how they use their assets and further expand the practical use of digital assets in global commerce.” Nischint Sanghavi, Head of Digital Currencies, APAC, said: “Consumers want payment methods that are flexible, convenient, and widely accepted. Visa is committed to working with partners to expand payment choices and connect new forms of value to established digital payments infrastructure. Through our partnership with Gate, we look forward to enabling more users to use digital assets wherever Visa is accepted, further enriching their everyday payment experience.” In recent years, digital assets have increasingly expanded into practical use cases such as payments, consumption, and cross-border commerce. By connecting digital asset accounts with Visa’s payments network, crypto cards offer users a more intuitive way to use digital assets and serve as an important gateway to integrating them into everyday economic activity. Learn more: https://www.gate.com/announcements/article/102107 About Gate Gate, founded in 2013 by Dr. Han, is one of the world’s leading cryptocurrency and integrated financial services platforms, spanning financial and everyday use cases including asset management, trading and payments. Serving over 61 million users globally, it supports trading across 5,300+ digital assets and 12,800+ stock assets, while providing access to a comprehensive range of TradFi assets, including metals, stocks, indices, forex, and commodities to meet users’ end-to-end needs across asset holding, trading and spending, trading and spending. As an industry benchmark, Gate was among the first platforms to implement 100% Proof of Reserves. Its ecosystem includes Gate Money, Gate Wallet, Gate Ventures, Gate for AI Agent, and a wide range of products and services. For more information, please visit: Website | X | Telegram | LinkedIn| Instagram | YouTube Disclaimer: This content does not constitute an offer, solicitation, or recommendation. You should always seek independent professional advice before making investment decisions. Note that Gate may restrict or prohibit certain services in specific jurisdictions. Card services are not intended for, and should not be accessed by, those in any jurisdictions where such services are prohibited by law. For more information, please read the User Agreement.