simplefunctions-cli
mcp
Uyari
Health Uyari
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 5 GitHub stars
Code Uyari
- process.env — Environment variable access in mcp-server.mjs
- network request — Outbound network request in mcp-server.mjs
- process.env — Environment variable access in src/client.ts
- network request — Outbound network request in src/client.ts
Permissions Gecti
- Permissions — No dangerous permissions requested
Purpose
This is an AI-powered terminal tool and MCP server that connects to Kalshi and Polymarket. It scans prediction markets, identifies trading edges using causal models, and executes trades autonomously.
Security Assessment
Overall Risk: Medium. The tool accesses environment variables to retrieve API keys and credentials, which is standard for trading platforms, and makes outbound network requests to exchange APIs and external data sources. No hardcoded secrets or dangerous system permissions were detected. However, the elevated risk stems from its core functionality: it is explicitly designed to execute financial trades and run an automated market-making engine. A misconfiguration, an LLM hallucination, or a malicious dependency update could result in direct monetary loss.
Quality Assessment
The project is licensed under the standard MIT license and was actively updated recently (pushed to 0 days ago). However, it suffers from extremely low community visibility with only 5 GitHub stars. Because it has not been widely peer-reviewed, hidden bugs in the trade execution logic or dependency tree remain highly likely.
Verdict
Use with caution — financial risk aside, the tool lacks the community vetting required for autonomous trading, so test heavily with paper-trading before connecting real funds.
This is an AI-powered terminal tool and MCP server that connects to Kalshi and Polymarket. It scans prediction markets, identifies trading edges using causal models, and executes trades autonomously.
Security Assessment
Overall Risk: Medium. The tool accesses environment variables to retrieve API keys and credentials, which is standard for trading platforms, and makes outbound network requests to exchange APIs and external data sources. No hardcoded secrets or dangerous system permissions were detected. However, the elevated risk stems from its core functionality: it is explicitly designed to execute financial trades and run an automated market-making engine. A misconfiguration, an LLM hallucination, or a malicious dependency update could result in direct monetary loss.
Quality Assessment
The project is licensed under the standard MIT license and was actively updated recently (pushed to 0 days ago). However, it suffers from extremely low community visibility with only 5 GitHub stars. Because it has not been widely peer-reviewed, hidden bugs in the trade execution logic or dependency tree remain highly likely.
Verdict
Use with caution — financial risk aside, the tool lacks the community vetting required for autonomous trading, so test heavily with paper-trading before connecting real funds.
AI-powered Kalshi & Polymarket trading bot. Scan 130K markets, detect edge with causal thesis models, backtest strategies, automated market making, and execute trades from the terminal. 42 commands, MCP server, REST API.
README.md
SimpleFunctions CLI — Kalshi & Polymarket Trading Bot
AI-powered prediction market trading bot and analysis CLI for Kalshi and Polymarket. Scans 130,000+ markets, detects mispricings with causal thesis models, backtests strategies, makes markets with an automated quote engine, and executes trades — all from the terminal.
npm i -g @spfunctions/cli
sf setup
What It Does
| Capability | Command | What other tools need |
|---|---|---|
| Scan 130K markets across Kalshi + Polymarket | sf scan "topic" |
Separate API clients for each venue |
| Screen by 6 quantitative indicators (IY, CRI, OR, EE, LAS, tau) | sf screen --iy-min 200 --tau-max 30 |
Custom scripts, no standard indicators |
| Real-time Level 2 orderbook with depth + slippage | sf book TICKER |
Kalshi WebSocket + custom parsing |
| Backtest entry/stop/TP strategies on historical data | sf backtest TICKER --entry-below 35 --stop 20 --tp 60 |
Build your own backtester |
| Causal thesis → probability → edge detection | sf create "thesis" → sf edges |
Nothing comparable |
| What-if scenario analysis on causal tree nodes | sf whatif ID --set "n1=0.9" |
Manual spreadsheets |
| Automated market making with inventory management | sf quoteengine start |
Custom HFT infrastructure |
| Intent-based order execution with triggers | sf intent buy TICKER 10 --trigger below:28 |
Manual order placement |
| 24/7 autonomous monitoring (news + X + LLM eval) | sf heartbeat ID |
Multiple tools stitched together |
| Real-time world state for AI agents (~800 tokens) | sf world |
No equivalent |
Quick Start
# Search for markets
sf scan "fed rate cuts 2026"
# Get the world state (what's happening right now)
sf world
# Inspect an orderbook
sf book KXCPI-26APR-T0.4
# Screen for high implied yield, tight spread markets
sf screen --iy-min 300 --las-max 0.03
# Backtest a strategy
sf backtest KXRECSSNBER-26 --entry-below 30 --stop 10 --tp 50
# Get AI-generated trade ideas
sf ideas
Trading Bot Features
Thesis-Driven Edge Detection
Unlike simple price-alert bots, SimpleFunctions builds causal probability models:
# Create a thesis → auto-generates causal tree
sf create "Iran conflict pushes oil above $130"
# See where markets are mispriced vs your thesis
sf edges
# Stress test: what if one assumption changes?
sf whatif ID --set "n1=0.9" --set "n4=0.5"
Automated Market Making (QuoteEngine v2)
# Paper-trade market making with 2¢ spread
sf quote create KXCPICORE-26APR-T0.3 --paper --spread 2 --size 5
# Full engine with WebSocket, inventory skew, fade-after-fill
sf quoteengine start
sf quoteengine status
Features: multi-layer quoting, inventory-based skew, fade-after-fill, configurable stop loss, thesis-biased mode.
Intent-Based Execution
# Buy when price drops below 28¢
sf intent buy KXRECSSNBER-26 50 --trigger below:28
# Buy at a specific time
sf intent buy TICKER 25 --trigger time:2026-04-15T14:00:00Z
# LLM-evaluated soft condition
sf intent buy TICKER 25 --soft "CPI comes in above consensus"
# Start the runtime daemon to evaluate triggers
sf runtime start --smart
24/7 Autonomous Monitoring
# Configure heartbeat: news every 4h, X scan every 4h, LLM eval every 15min
sf heartbeat ID --news-interval 240 --x-interval 240 --model cheap --budget 5
# Enable closed-loop trading (auto-entry from strategies)
sf heartbeat ID --closed-loop-entry --closed-loop-exit
All Commands (42)
Expand full command referenceThesis Management
| Command | Description |
|---|---|
sf list |
List all theses with confidence scores |
sf get <id> |
Full thesis details with causal tree |
sf context [id] |
Market snapshot (no id) or thesis context (with id) |
sf create "thesis" |
Create thesis with auto-generated causal tree |
sf signal <id> "news" |
Inject observation for next evaluation |
sf evaluate <id> |
Force deep re-evaluation |
sf augment <id> |
Evolve causal tree with new nodes |
sf heartbeat <id> |
Configure 24/7 monitoring |
Market Data
| Command | Description |
|---|---|
sf scan "keywords" |
Search Kalshi + Polymarket by keyword |
sf screen [filters] |
Screen 130K markets by IY, CRI, OR, EE, LAS, tau |
sf book <ticker> |
Level 2 orderbook with depth |
sf liquidity [topic] |
Orderbook liquidity scanner |
sf markets |
Traditional markets (SPY, VIX, TLT, GLD, USO, etc.) |
sf query "question" |
LLM-enhanced prediction market search |
sf forecast <event> |
P50/P75/P90 distribution over time |
World Model
| Command | Description |
|---|---|
sf world |
Real-time world state (~800 tokens) |
sf world --delta |
What changed since last check |
sf world --focus energy,geo |
Deep coverage on specific topics |
sf ideas |
S&T-style trade recommendations |
Trading & Execution
| Command | Description |
|---|---|
sf edges |
Top mispricings across all theses |
sf whatif <id> |
Scenario analysis with node overrides |
sf backtest <ticker> |
Strategy backtesting |
sf intent buy/sell |
Conditional order with triggers |
sf buy/sell <ticker> <qty> |
Direct order placement |
sf quote create <ticker> |
Market making quote |
sf quoteengine start/stop |
Automated market making engine |
sf runtime start |
Execution daemon for intents |
Portfolio
| Command | Description |
|---|---|
sf positions |
Kalshi + Polymarket positions |
sf balance |
Account balance |
sf orders |
Resting orders |
sf fills |
Recent trade fills |
sf settlements |
Settled contracts with P&L |
sf performance |
P&L over time |
MCP Server — Connect to Claude, Cursor, or Any AI Agent
claude mcp add simplefunctions --url https://simplefunctions.dev/api/mcp/mcp
25 tools for prediction market data, thesis management, trading, and X/Twitter sentiment. Listed on the MCP Registry.
Agent Integration
Every command supports --json for structured output. Pipe into any agent framework:
# LangChain / CrewAI / OpenAI Agents — use as a subprocess
sf scan "recession" --json | your-agent-script
# Or use the dedicated SDK packages
npm i prediction-market-context # world state + context
npm i prediction-market-edge-detector # edge scanning
npm i agent-world-awareness # one-line world injection
Python: pip install simplefunctions-ai
How It Compares
| SimpleFunctions CLI | Kalshi API + scripts | Other trading bots | |
|---|---|---|---|
| Markets | Kalshi + Polymarket unified | Kalshi only | Usually one venue |
| Analysis | Causal trees, 6 indicators, what-if | Raw prices | LLM opinion |
| Execution | Intents, quote engine, runtime daemon | Manual orders | Basic buy/sell |
| Monitoring | 24/7 heartbeat with news + X + LLM | None | Price alerts only |
| Data | 130K markets, Level 2 orderbook | Kalshi only | Limited |
Documentation
Full docs, API reference, and guides: simplefunctions.dev/docs
License
MIT
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