Tech Stocks Fall as Next Week Brings Big Tests for the AI Trade

 Tech Stocks Fall as Next Week Brings Big Tests for the AI Trade

Markets P.M. | Nov. 13, 2026

Technology stocks sold off sharply on Friday, with Nvidia extending its steep decline as the chip giant’s leadership crisis rattled the broader artificial-intelligence trade. Investors are now bracing for two pivotal events next week that could determine whether the AI rally stabilizes or deepens: Nvidia’s quarterly earnings and Anthropic’s blockbuster initial public offering.

Nvidia Slides Again as Leadership Turmoil Deepens

Nvidia shares fell 4.3% to close at $159.11, marking a second consecutive session of heavy losses and putting the stock on track for its longest weekly losing streak since October 2012 . The selloff followed Thursday’s abrupt resignation of CEO Jensen Huang amid scrutiny over his relationship with a businesswoman suspected by U.S. authorities of spying for Beijing .

The contagion spread across the semiconductor complex. The Philadelphia Semiconductor Index dropped 6%, with Advanced Micro Devices falling 10% and Broadcom losing 7% . AI-adjacent names were hit even harder: CoreWeave and Nebius each shed roughly 10%, while data-center-related firms including IREN, Applied Digital, and Core Scientific declined between 3% and 9% .

The tech-heavy Nasdaq-100 fell 2%, on pace to erase approximately $800 billion in market value, while the Philadelphia chip index faced a daily wipeout exceeding $560 billion .

“It’s a short-term death blow to the industry because the amount of vendor financing and financing guarantees that Nvidia and Jensen Huang stepped into are now over,” said Thomas Hayes, chairman of Great Hill Capital LLC .

Next Week’s Twin Tests for the AI Trade

Two events loom large over the sector.

Nvidia Earnings (Tuesday): Finance chief and interim CEO Colette Kress will face investors seeking clarity on the web of financing deals Huang structured—arrangements that drove demand for Nvidia chips but raised questions about circular funding flows between the chipmaker and its customers .

Anthropic IPO (Thursday): The Claude maker is expected to price its offering Wednesday and begin trading Thursday, seeking to raise as much as $90.4 billion at a valuation of approximately $1.4 trillion . The offering includes 825 million shares priced at $70 to $78 each, with strategic investors including Nvidia, Microsoft, Google, and AMD committing $26 billion in a concurrent private placement .

“Something that will be interesting to see is if all the supply of new equity is going to be absorbed in a way that doesn’t result in selling other AI stocks to kind of fund that,” said Angelo Kourkafas, senior global investment strategist at Edward Jones .

Anthropic CEO Aims to ‘Underpromise and Overdeliver’

Speaking Friday in a livestreamed fireside chat with JPMorgan CEO Jamie Dimon at the bank’s Manhattan headquarters, Anthropic CEO Dario Amodei struck a measured tone on the IPO.

“I hope we will underpromise and overdeliver,” Amodei said .

The comment reflects the tempered expectations surrounding the listing. Just weeks ago, investors anticipated a valuation as high as $2 trillion; that figure has since been revised down to approximately $1.4 trillion amid rising borrowing costs, political backlash over data-center development, and the turmoil surrounding Nvidia .

Amodei described securing sufficient computing power without overspending as Anthropic’s “deepest business dilemma.” The company disclosed aggregate computing and infrastructure commitments of approximately $895.4 billion, with roughly $69.8 billion due in the twelve months following September 30, 2026 . For the September quarter, Anthropic reported revenue of $16.96 billion—up 47% from the prior quarter—alongside a net loss of $23.3 billion .

Market Breadth

The selling was broad but not universal. Among the Magnificent Seven, Apple and Meta were the only gainers, each rising less than 1%. Microsoft slipped 0.4% after a more than 12-hour outage of Outlook and Teams . The tech sector was the biggest loser on the S&P 500, dropping 3%, with six of eleven major sectors in negative territory .

In economic data, October producer prices came in slightly softer than expected but still showed a 6.5% year-over-year increase—the largest annual gain since November 2022 .

Key Levels (as of Nov. 13 close/late trading):

Asset Move

Nvidia (NVDA) -4.3% to $159.11

AMD -10%

Broadcom (AVGO) -7%

Philadelphia Semi Index -6%

Nasdaq-100 -2%

CoreWeave -10%

Sources: WSJ, Reuters, Moneycontrol, Yahoo Finance

Building a Crypto Trading Bot with Claude Code: No 1 Guide

 Building a Crypto Trading Bot with Claude Code: Practical Guide, Architecture, and Risk Management

The intersection of generative AI and the volatile cryptocurrency market has sparked immense interest among developers and traders alike. Anthropic’s terminal and desktop-based agentic coding tool, Claude Code, has fundamentally changed how software systems are built. Unlike traditional chatbots that simply output snippets of code to copy and paste, this tool acts as an autonomous development agent that understands your entire repository, runs tests, connects to data sources, and iterates on code in real time.

The core question for traders and engineers is no longer whether you can build a crypto bot with AI, but how to engineer one responsibly, securely, and sustainably.

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How Claude Code Transforms Algorithmic Trading Development

Historically, building an algorithmic trading bot required weeks of writing boilerplate code for exchange API integrations, network error handling, backtesting engines, and monitoring dashboards. Claude Code compresses this lifecycle from weeks into days through several core capabilities:

• Project-Level Context (CLAUDE.md). Developers can establish a foundational configuration file at the root of the repository that dictates architectural rules, strict risk-management limits, and error-handling protocols across every coding session.
• Subagents and Specialized Skills. Complex workflows can be delegated to specialized subagents, such as an agent dedicated to technical indicator logic, another for writing comprehensive unit tests, and a security auditor (/security-review) to scan for vulnerabilities.
• Direct Tool Integration via MCP. Using the Model Context Protocol (MCP), Claude Code can connect directly to local databases (like PostgreSQL), manage GitHub repositories, and pull live market data without requiring cumbersome glue code.
• Autonomous Testing Loops (/loop). The agent can execute backtesting simulations, inspect performance logs for bugs or anomalies, and iteratively patch the codebase until all validation checks pass.

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Recommended Architecture for an AI-Built Trading Bot

Based on the real-world experience of developers building trading systems with Claude Code, the most common mistake is asking the model to write "a single script that trades and makes money." A resilient system requires a strict separation of concerns across distinct software layers:

System Layer	Role in the Trading Pipeline	Common Tools & Technologies
Data Ingestion	Fetches real-time prices, trading volumes, order books, and sentiment data.	CCXT library, exchange WebSockets (Binance, Kraken, Coinbase).
Strategy Engine	Processes technical indicators (RSI, MACD, moving averages) and detects market regimes.	Python (Pandas, NumPy), statistical models, or targeted LLM agents.
Risk Management	Calculates position sizing, enforces Stop-Loss/Take-Profit levels, and triggers circuit breakers.	Hardcoded, deterministic logic completely independent of LLM discretion.
Execution Layer	Submits buy/sell orders, manages slippage, and verifies fee structures.	Exchange REST APIs, asynchronous task queues, and exponential backoff retry logic.
Logging & Monitoring	Records every trade decision, tracks Profit & Loss (PnL), and sends real-time alerts.	Databases (PostgreSQL / SQLite), web dashboards, or Telegram alert bots.

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Step-by-Step Workflow to Build Your Bot

1. Define Hard Guardrails First

Before generating a single line of trading logic, define your non-negotiable rules inside CLAUDE.md. Explicitly instruct the agent that the bot must never risk more than a fixed percentage of total capital per trade (typically 1% to 2%), and that every open position must include an exchange-level Stop-Loss order.

2. Build a Realistic Backtesting Engine

Testing your strategy against historical data is critical, but naive backtests often produce misleading results. Instruct Claude Code to build a simulation engine that accurately models exchange fees (Maker/Taker rates) and slippage. A high-frequency strategy that looks profitable on paper can quickly bleed capital once real-world transaction costs are factored in.

3. Validate in Paper Trading (Testnet)

Never deploy a newly generated bot directly onto a live account with real funds. Major crypto exchanges provide sandbox environments (Testnets) that simulate order execution against live market feeds using virtual currency. Run your bot in paper-trading mode for several weeks, and use Claude Code to analyze execution logs for race conditions, API rate-limit errors, or logic flaws.

4. Enforce Strict API Security

Securing your exchange credentials is paramount. Ensure API keys are stored strictly in local environment variables (a .env file excluded via .gitignore so it is never pushed to GitHub). Additionally, configure your exchange API permissions to allow Read and Spot Trading only—explicitly disabling Withdrawals and Margin/Leverage unless your strategy specifically requires and accounts for them.

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Limitations and Risks You Must Consider

While AI coding tools dramatically accelerate engineering velocity, maintaining a realistic perspective on both AI limitations and crypto market dynamics is essential:

• AI Writes Code, Not Market Alpha. Large language models are thoroughly trained on classic public strategies like Grid Trading or Moving Average Crossovers. Because these strategies are widely known and heavily competed over, their edge is often minimal. True market alpha must come from your own research, unique data sources, and market intuition.
• The Danger of Overfitting. If you instruct an AI to optimize strategy parameters until historical backtest returns look exceptional, it will likely curve-fit the model to past noise—resulting in poor performance when live market conditions shift.
• Latency and Token Costs. If your bot's architecture relies on making live API calls to an LLM to decide every trade in real time, you will face ongoing token expenses and multi-second response latencies. Crypto markets move in milliseconds; high-frequency execution must rely on fast, deterministic code rather than live LLM inference.
• Inherent Financial Risk. Cryptocurrency trading carries substantial risk of capital loss due to extreme volatility, flash crashes, liquidity gaps, and exchange outages. Never allocate capital to an automated system that you cannot afford to lose.

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Conclusion

Using Claude Code transforms crypto bot development from a daunting software engineering project into an accessible, streamlined workflow. It excels at scaffolding infrastructure, integrating exchange APIs, writing test suites, and debugging complex edge cases. However, long-term financial performance still depends entirely on the soundness of your trading edge, strict deterministic risk controls, and continuous human oversight.

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Forex Market Review (Asian Session) – Fed's inflation concern weakens dollar; aussie, kiwi at two-year high

Forex Market Review (Asian Session) – Fed's inflation concern weakens dollar; aussie, kiwi at two-year high

Forex Market Review (Asian Session) – Fed’s inflation concern weakens dollar; aussie, kiwi at two-year high

Posted on July 27, 2017 at 7:47 am GMT

Maja Rakic, XM Investment Research Desk


It was a relatively quiet data day in forex markets prompting traders to dwell over yesterday’s events, namely the Federal Open Market Committee (FOMC) meeting. As a result of the Federal Reserve’s dovish tone, the weakness in the US dollar unfolded, with the dollar index hitting a fresh 13-month low. The Australian and New Zealand dollars jumped to two-year highs as traders sold off the greenback.