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MonteWalk

Sleeping

symbol (str): Ticker symbol (e.g., 'AAPL', 'BTC-USD')
interval (Literal): Data interval - "1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo" (default: "1d")
period (Literal): Data period - "1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max" (default: "1y")

Returns: List[Dict[str, Any]] - List of OHLCV data dictionaries

Example:

get_price("AAPL", interval="1d", period="1mo")
# Returns: [{"Date": "2024-01-01", "Open": 150.0, "High": 155.0, ...}, ...]

`get_fundamentals`

Fetches fundamental metrics for a symbol.

Parameters:

symbol (str): Ticker symbol

Returns: Dict[str, Any] - Dictionary with P/E ratio, market cap, margins, etc.

Example:

get_fundamentals("AAPL")
# Returns: {"pe_ratio": 28.5, "market_cap": 2800000000000, ...}

`get_orderbook`

Placeholder for order book data (currently returns mock data).

Parameters:

symbol (str): Ticker symbol

Returns: str - Order book representation

Execution

`place_order`

Submits a market or limit order to the paper trading exchange with simulated slippage and transaction costs.

Parameters:

symbol (str): Ticker symbol
side (Literal["buy", "sell"]): Order side
qty (float): Quantity to trade
order_type (Literal["market", "limit"]): Order type (default: "market")
limit_price (Optional[float]): Required if order_type is "limit"

Returns: str - Execution confirmation or error message

Example:

place_order("AAPL", "buy", 10, "market")
# Returns: "ORDER FILLED: Bought 10 shares of AAPL at $150.08..."

Pre-trade Risk Checks:

Validates trade doesn't exceed 50% of available cash
Ensures positive quantity
Checks symbol price availability

`cancel_order`

Cancels a pending order (currently a placeholder for paper trading).

Parameters:

order_id (str): Order ID to cancel

Returns: str - Cancellation confirmation

`get_positions`

Returns current portfolio state (cash and positions).

Returns: Dict[str, Any] - Portfolio dictionary

Example:

get_positions()
# Returns: {"cash": 95000.0, "positions": {"AAPL": 10, "MSFT": 5}}

`flatten`

Closes all open positions at market price.

Returns: str - Summary of closed positions

Example:

flatten()
# Returns: "Sold 10 AAPL at $150.00. Sold 5 MSFT at $380.00. All positions closed."

Risk Management

`portfolio_risk`

Calculates portfolio volatility (annualized standard deviation).

Returns: str - Portfolio volatility percentage

Example:

portfolio_risk()
# Returns: "Portfolio Volatility: 18.45% (annualized)"

`var`

Calculates Value at Risk (VaR) using historical simulation.

Parameters:

confidence (float): Confidence level (default: 0.95)

Returns: str - VaR estimate

Example:

var(confidence=0.95)
# Returns: "VaR (95% confidence): $2,450 over 1 day"

`max_drawdown`

Calculates the maximum drawdown of the portfolio.

Returns: str - Maximum drawdown percentage

Example:

max_drawdown()
# Returns: "Max Drawdown: -12.34%"

`monte_carlo_simulation`

Runs Monte Carlo simulation using Geometric Brownian Motion to forecast potential portfolio paths.

Parameters:

simulations (int): Number of simulation paths (default: 1000)
days (int): Forecast horizon in days (default: 252)

Returns: str - Simulation summary with percentiles

Example:

monte_carlo_simulation(simulations=500, days=30)
# Returns: "Monte Carlo Simulation (500 paths, 30 days)
#           5th percentile: -$1,200
#           50th percentile: +$450
#           95th percentile: +$3,100"

Backtesting

`run_backtest`

Backtests a simple Moving Average Crossover strategy with transaction costs.

Parameters:

symbol (str): Ticker symbol
fast_ma (int): Fast moving average period
slow_ma (int): Slow moving average period
start_date (str): Start date (default: "2020-01-01")
end_date (str): End date (default: "2023-12-31")

Returns: str - Backtest results with returns and metrics

Example:

run_backtest("AAPL", fast_ma=10, slow_ma=50, start_date="2023-01-01", end_date="2023-12-31")
# Returns: "Backtest Results: Total Return: 15.2%, Buy & Hold: 48.5%, Sharpe: 0.65"

`walk_forward_analysis`

Performs rolling walk-forward analysis for out-of-sample validation.

Parameters:

symbol (str): Ticker symbol
start_date (str): Start date (default: "2020-01-01")
end_date (str): End date (default: "2023-12-31")
train_months (int): Training window in months (default: 12)
test_months (int): Testing window in months (default: 3)

Returns: str - Walk-forward results summary

Example:

walk_forward_analysis("AAPL", train_months=12, test_months=3)
# Returns: "Walk Forward Results: Window 1: Train 12.3%, Test -2.1%..."

Technical Analysis

`compute_indicators`

Calculates technical indicators (RSI, MACD, Bollinger Bands).

Parameters:

symbol (str): Ticker symbol
indicators (List[str]): List of indicators (default: ["RSI", "MACD"])

Returns: str - JSON string of indicator values

Example:

compute_indicators("AAPL", indicators=["RSI", "MACD", "BBANDS"])
# Returns: JSON with indicator values for last 10 days

`rolling_stats`

Computes rolling mean and volatility.

Parameters:

symbol (str): Ticker symbol
window (int): Rolling window size (default: 20)

Returns: str - JSON string of rolling statistics

`get_technical_summary`

Performs comprehensive technical analysis and returns a clear Buy/Sell/Neutral signal.

Parameters:

symbol (str): Ticker symbol

Returns: str - Technical summary with signal

Example:

get_technical_summary("AAPL")
# Returns: "Technical Summary for AAPL:
#           Signal: BUY (Score: 2)
#           Price: $175.50
#           - RSI is Neutral (55.32)
#           - MACD Bullish Crossover
#           - Price above 50 SMA..."

Scoring Logic:

RSI: Oversold (<30) = +1, Overbought (>70) = -1
MACD: Bullish crossover = +1, Bearish = -1
50 SMA: Price above = +1, below = -1
200 SMA: Price above = +1, below = -1

Signals:

Score ≥2: STRONG BUY
Score =1: BUY
Score =0: NEUTRAL
Score =-1: SELL
Score ≤-2: STRONG SELL

Portfolio Optimization

`mean_variance_optimize`

Calculates optimal portfolio weights using Mean-Variance Optimization (maximizes Sharpe ratio).

Parameters:

tickers (List[str]): List of ticker symbols
lookback (str): Lookback period (default: "1y")

Returns: str - Optimal weights dictionary

Example:

mean_variance_optimize(["AAPL", "MSFT", "GOOGL"])
# Returns: "Optimal Weights (Max Sharpe): {'AAPL': 0.45, 'MSFT': 0.32, 'GOOGL': 0.23}"

`risk_parity`

Calculates weights based on Inverse Volatility (Naive Risk Parity).

Parameters:

tickers (List[str]): List of ticker symbols

Returns: str - Risk parity weights

Example:

risk_parity(["AAPL", "MSFT"])
# Returns: "Risk Parity Weights: {'AAPL': 0.52, 'MSFT': 0.48}"

News & Sentiment

`get_news`

Retrieves recent news headlines for a symbol (yfinance → NewsAPI → GNews fallback).

Parameters:

symbol (str): Ticker symbol
max_items (int): Maximum news items (default: 10)

Returns: str - JSON string of news articles

Example:

get_news("AAPL", max_items=5)
# Returns: JSON array of news: [{"title": "...", "publisher": "...", "link": "..."}, ...]

`analyze_sentiment`

Analyzes sentiment of text using FinBERT (Modal).

Parameters:

text (str): Text to analyze

Returns: Dict[str, Any] - Sentiment analysis with polarity and subjectivity

Example:

analyze_sentiment("Apple announces record-breaking earnings!")
# Returns: {"text": "Apple announces...", "polarity": 0.98, "subjectivity": 0.0, "classification": "POSITIVE", "model": "FinBERT (Modal Public)"}

Classification:

Polarity >0.2: POSITIVE
Polarity <-0.2: NEGATIVE
Otherwise: NEUTRAL

`get_symbol_sentiment`

Fetches news and calculates aggregate sentiment for a symbol.

Parameters:

symbol (str): Ticker symbol

Returns: str - Sentiment summary

Example:

get_symbol_sentiment("AAPL")
# Returns: "Sentiment Analysis for AAPL (10 articles):
#           Average Polarity: 0.35
#           Market Sentiment: BULLISH"

Watchlist

`add_to_watchlist`

Adds a symbol to the monitoring watchlist.

Parameters:

symbol (str): Ticker symbol

Returns: str - Confirmation message

Example:

add_to_watchlist("TSLA")
# Returns: "Added TSLA to watchlist."

`remove_from_watchlist`

Removes a symbol from the watchlist.

Parameters:

symbol (str): Ticker symbol

Returns: str - Confirmation message

Cryptocurrency

`get_crypto_price`

Gets current price of a cryptocurrency using CoinGecko API.

Parameters:

coin_id (str): CoinGecko ID (e.g., 'bitcoin', 'ethereum', 'solana')
vs_currency (str): Currency to compare (default: "usd")

Returns: Dict[str, Any] - Price data

Example:

get_crypto_price("bitcoin")
# Returns: {"coin": "bitcoin", "price": 97832.0, "market_cap": ..., "24h_change": 1.34}

`get_crypto_market_data`

Gets comprehensive market data for a cryptocurrency.

Parameters:

coin_id (str): CoinGecko ID

Returns: str - Formatted market data

Example:

get_crypto_market_data("ethereum")
# Returns: "Crypto Market Data: ETHEREUM
#           Current Price: $3,450.23
#           24h Change: +2.5%
#           All-Time High: $4,878.26..."

`get_trending_crypto`

Gets the top trending cryptocurrencies in the last 24 hours.

Returns: str - List of trending coins

Example:

get_trending_crypto()
# Returns: "=== TRENDING CRYPTOCURRENCIES (24h) ===
#           1. Bitcoin (BTC) - Rank #1
#           2. Dogecoin (DOGE) - Rank #8..."

`search_crypto`

Searches for cryptocurrencies by name or symbol.

Parameters:

query (str): Search term

Returns: str - Search results

Example:

search_crypto("cardano")
# Returns: "=== SEARCH RESULTS FOR 'cardano' ===
#           • Cardano (ADA) - ID: cardano - Rank #9..."

Logging

`log_action`

Logs an agent action or reasoning step for audit purposes.

Parameters:

action_type (str): Category (e.g., 'REASONING', 'TRADE_DECISION', 'ERROR')
details (str): Description of the action

Returns: str - Confirmation

Example:

log_action("TRADE_DECISION", "Decided to buy AAPL due to positive technical signals")
# Returns: "Action logged successfully."

Resources

Resources provide live, read-only data that is automatically available to the AI without explicit tool calls.

`portfolio://summary`

Returns a live summary of the portfolio (cash, positions, equity).

Format:

=== PORTFOLIO SUMMARY ===
Cash: $95,000.00
Positions: 2
--- Holdings ---
AAPL: 10 shares
MSFT: 5 shares

`market://watchlist`

Returns live prices for all symbols in the watchlist.

Format:

=== MARKET WATCHLIST ===
AAPL: $175.50
TSLA: $245.30

`news://latest`

Returns the latest headline for each symbol in the watchlist.

Format:

=== LATEST NEWS (Watchlist) ===
[AAPL] Apple announces new AI features (Bloomberg)
[TSLA] Tesla hits production milestone (Reuters)

`crypto://trending`

Returns the top 10 trending cryptocurrencies.

Format:

=== TRENDING CRYPTOCURRENCIES (24h) ===
1. Bitcoin (BTC) - Rank #1
2. Ethereum (ETH) - Rank #2
...

Prompts

Prompts are pre-defined workflows that guide the AI through complex, multi-step analyses.

`morning_briefing`

Generates a comprehensive morning trading briefing.

Context Included:

Current portfolio (cash + positions)
Watchlist symbols

Workflow:

Review performance of held positions
Check watchlist for significant moves
Summarize top market news
Suggest risk adjustments

Usage:

@QuantAgent /morning_briefing

`analyze_ticker`

Performs a deep-dive analysis on a specific ticker.

Parameters:

symbol (str): Ticker to analyze

Workflow:

Fetch price trends (1y and 5d)
Get fundamentals (P/E, margins)
Analyze news sentiment
Compute technical indicators

Output:

Executive Summary (Buy/Sell/Hold)
Key Risks
Technical Levels (Support/Resistance)

Usage:

@QuantAgent /analyze_ticker TSLA

Quick Reference Card

Category	Tool Count	Key Tools
Market Data	3	`get_price`, `get_fundamentals`
Execution	4	`place_order`, `get_positions`, `flatten`
Risk	4	`portfolio_risk`, `var`, `monte_carlo_simulation`
Backtesting	2	`run_backtest`, `walk_forward_analysis`
Technical	3	`get_technical_summary`, `compute_indicators`
Portfolio Opt	2	`mean_variance_optimize`, `risk_parity`
News	3	`get_news`, `get_symbol_sentiment`
Watchlist	2	`add_to_watchlist`, `remove_from_watchlist`
Crypto	4	`get_crypto_price`, `get_trending_crypto`
Logging	1	`log_action`
Total	25

Common Workflows

Execute a Trade

1. get_price("AAPL") - Check current price
2. get_technical_summary("AAPL") - Get buy/sell signal
3. portfolio_risk() - Check current risk
4. place_order("AAPL", "buy", 10) - Execute

Risk Analysis

1. get_positions() - See current holdings
2. portfolio_risk() - Check volatility
3. var(0.99) - Check worst-case scenario
4. monte_carlo_simulation(1000, 30) - Forecast outcomes

Research a Stock

1. get_fundamentals("TSLA") - Check valuation
2. get_news("TSLA", 10) - Read headlines
3. get_symbol_sentiment("TSLA") - Gauge mood
4. get_technical_summary("TSLA") - Technical signal

Error Handling

All tools implement robust error handling:

Market Data: Returns error if symbol not found or API unavailable
Execution: Pre-trade risk checks prevent invalid trades
Risk Tools: Return "Portfolio empty" if no positions
News: Automatic fallback: yfinance → NewsAPI → GNews
Crypto: Graceful degradation if CoinGecko API fails

Rate Limits & Quotas

API	Free Tier Limit	Status
yfinance	~2,000/hour	No key required
NewsAPI	100 requests/day	Optional key
CoinGecko	10-50 calls/minute	No key required
GNews	Unlimited (scraped)	No key required

Support & Documentation

Main README: README.md
VSCode Setup: VSCODE_SETUP.md
NewsAPI Setup: NEWSAPI_SETUP.md
Audit Report: AUDIT_REPORT.md

QuantAgent API Reference

Table of Contents

Tools

Market Data

get_price

get_fundamentals

get_orderbook

Execution

place_order

cancel_order

get_positions

flatten

Risk Management

portfolio_risk

var

max_drawdown

monte_carlo_simulation

Backtesting

run_backtest

walk_forward_analysis

Technical Analysis

compute_indicators

rolling_stats

get_technical_summary

Portfolio Optimization

mean_variance_optimize

risk_parity

News & Sentiment

get_news

analyze_sentiment

get_symbol_sentiment

Watchlist

add_to_watchlist

remove_from_watchlist

Cryptocurrency

get_crypto_price

get_crypto_market_data

get_trending_crypto

search_crypto

Logging

log_action

Resources

portfolio://summary

market://watchlist

news://latest

crypto://trending

Prompts

morning_briefing

analyze_ticker

Quick Reference Card

Common Workflows

Execute a Trade

Risk Analysis

Research a Stock

Error Handling

Rate Limits & Quotas

Support & Documentation

`get_price`

`get_fundamentals`

`get_orderbook`

`place_order`

`cancel_order`

`get_positions`

`flatten`

`portfolio_risk`

`var`

`max_drawdown`

`monte_carlo_simulation`

`run_backtest`

`walk_forward_analysis`

`compute_indicators`

`rolling_stats`

`get_technical_summary`

`mean_variance_optimize`

`risk_parity`

`get_news`

`analyze_sentiment`

`get_symbol_sentiment`

`add_to_watchlist`

`remove_from_watchlist`

`get_crypto_price`

`get_crypto_market_data`

`get_trending_crypto`

`search_crypto`

`log_action`

`portfolio://summary`

`market://watchlist`

`news://latest`

`crypto://trending`

`morning_briefing`

`analyze_ticker`