交易与金融

TradingAgents 将基本面、情绪、新闻和技术分析与多空研究讨论、交易员及风险管理角色结合起来。 设置 TYPESAFE_API_KEY 后，Jev 会排除无关的 StockTwits 和 Reddit 帖子并判断立场；未设置时帖子不经筛选。

这套自托管交易系统涵盖 Python 策略、回测、模拟交易、实盘执行与监控。 Jev 评估订单、策略、风险敞口、持仓和预算上下文，为入场过滤返回类型化选择及概率。

机器人随 Monad 区块更新仅挂单限价报价，并将盘口、订单与成交事件推送到仪表盘。 配置 MODEL=jev 和 TYPESAFE_AI_API_KEY 后由 Jev 选择买或卖；默认 mock 模型只是动量启发式。

AI Stock 汇集行情、新闻和 AI 分析，用于股票研究、筛选及模拟交易。 可选 JEV Decisions only 返回买入、卖出、持有及概率、置信度；研究聊天与选股范围仍需 LLM。 配置模型和数据源，选择股票，再检查已保存报告的证据、日期与风险。

Live Jev trader on Hyperliquid

AI-driven intraday trading bot for Indian stocks. Jev ranks the Nifty 50 every 15s; code sizes each trade and places the stop; orders go live through Zerodha Kite or Kotak Neo. Day replay, kill switch, daily loss halt, terminal dashboard.

5m BTC Up/Down Polymarket trading agent using Typesafe Jev as the decision layer & TUI

Trading bot with the all new TypeSafe AI's first system one model named as Jev

24/7 market-making system around Jev (TypeSafe System One) decisions: deterministic state, calibrated judgments, hard risk vetoes.

Read-only trading journal and review harness: Jev typed judgments, agent integration, and a reproducible finance benchmark. No orders, no advice.

Unofficial study: Jev-style parallel typed decisions on stock 1.5B-8B models on an Apple Silicon laptop. Benchmarks, research notes, and a Hugging Face Space demo.

An experimental JEV-powered framework for forecasting short-term stock price direction from structured market data.

Polymarket trading bot driven by Jev (TypeSafe AI) via OpenRouter

Modular trading framework for Mid-Frequency Trading

Backtest Jev (TypeSafe) as a BUY/SELL/HOLD trader on NQ L10 order-book data

JEV-powered market decision bot for stocks, crypto and memes. State in, BUY/SELL/HOLD/AVOID out, paper by default

Live Moomoo trading dashboard with TypeSafe Jev market reviews

Desktop app for paper-trading stocks and crypto on live prices, with TypeSafe's Jev making the calls and your stops, targets and limits enforced in code. Windows and macOS; never touches real money.

Experimental multi-horizon BTC signal generator using TypeSafe Jev probabilities and Binance market data.

Hands-on LangChain examples of Jev, TypeSafe AI's decision model: support ticket triage, model routing, reply guardrails, tool selection and finance-inbox fraud checks. An LLM writes; Jev decides.

Self-hosted AI trading intelligence platform - scoring engine, two-model AI analyst (Claude + TypeSafe Jev), risk engine, discipline guardian, backtester, React dashboard

Options trading bot (backtest + Alpaca paper only) with TypeSafe Jev as the decision core

Typed System-1 decisions (Laya/Jev) as the judgment layer of a quant research stack, with Claude as System 2. Requirements → design → code → experiments.

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