Kye Gomez
1 month ago
parent
a16b815bde
commit
9c78b39291
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from swarms.structs.deep_research_swarm import DeepResearchSwarm
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swarm = DeepResearchSwarm(
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name="Deep Research Swarm",
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description="A swarm that conducts comprehensive research across multiple domains",
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max_loops=1,
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)
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swarm.run(
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"What are the biggest gas and oil companies in russia? Only provide 3 queries"
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)
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import requests
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from dotenv import load_dotenv
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from swarms import Agent, SequentialWorkflow
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load_dotenv()
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# Technical Analysis Agent
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TECHNICAL_ANALYST_PROMPT = """
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You are an expert Technical Analysis agent specializing in cryptocurrency markets. Your role is to:
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1. Analyze price patterns, trends, and indicators with specific numerical data.
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2. Identify key support and resistance levels with exact price points.
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3. Evaluate market momentum and volume patterns using quantitative metrics.
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4. Provide detailed technical insights based on chart patterns, including Fibonacci retracement levels and moving averages.
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5. Monitor and interpret trading indicators (RSI, MACD, Bollinger Bands) with specific values.
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When analyzing data, focus on:
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- Price action and volume analysis, including percentage changes and volume spikes.
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- Chart pattern recognition, such as head and shoulders, double tops/bottoms, and triangles.
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- Indicator divergences and confluences, providing specific indicator values.
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- Market structure and trend analysis, including bullish/bearish trends with defined price ranges.
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- Support/resistance levels and price zones, specifying exact price levels.
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Provide your analysis in a clear, structured format with bullet points for key findings, including numerical data.
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Emphasize objective technical analysis without making direct price predictions.
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"""
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technical_agent = Agent(
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agent_name="Technical-Analyst",
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system_prompt=TECHNICAL_ANALYST_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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)
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# Market Sentiment Agent
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SENTIMENT_ANALYST_PROMPT = """
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You are a Market Sentiment Analysis specialist focusing on social and market psychology. Your tasks:
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1. Analyze social media sentiment across platforms with specific sentiment scores.
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2. Monitor community engagement metrics, including likes, shares, and comments.
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3. Track market fear/greed indicators with numerical values.
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4. Evaluate news impact on market sentiment, providing specific examples.
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5. Assess institutional interest and whale activity, detailing transaction sizes.
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Focus areas:
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- Social media sentiment trends, providing percentage changes.
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- Community growth and engagement metrics, including follower counts.
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- News sentiment analysis, quantifying positive/negative impacts.
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- Institutional investment flows, detailing specific amounts.
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- Whale wallet activity monitoring, providing transaction details.
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Provide objective sentiment analysis using multiple data sources.
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Emphasize data-driven insights rather than speculation.
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"""
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# Risk Management Agent
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RISK_MANAGER_PROMPT = """
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You are a Risk Management specialist focused on market risk assessment. Your role involves:
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1. Identifying potential market risks and vulnerabilities with specific examples.
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2. Analyzing market volatility and liquidity using quantitative measures.
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3. Evaluating correlation with broader markets, providing correlation coefficients.
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4. Assessing regulatory and operational risks, detailing specific regulations.
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5. Monitoring market manipulation indicators with defined thresholds.
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Key focus areas:
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- Volatility analysis and risk metrics, including standard deviation and beta values.
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- Liquidity depth assessment, providing order book metrics.
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- Correlation analysis with major assets, detailing specific correlations.
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- Regulatory compliance risks, specifying relevant regulations.
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- Smart contract and protocol risks, detailing potential vulnerabilities.
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Provide comprehensive risk assessment with clear risk ratings and mitigation strategies.
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Focus on identifying both obvious and subtle risk factors.
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"""
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risk_agent = Agent(
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agent_name="Risk-Manager",
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system_prompt=RISK_MANAGER_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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)
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# Macro Analysis Agent
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MACRO_ANALYST_PROMPT = """
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You are a Macro Analysis specialist focusing on broader market context. Your role involves:
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1. Analyzing global economic trends with specific indicators.
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2. Evaluating crypto market cycles, providing cycle duration and phases.
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3. Monitoring regulatory developments, detailing specific changes.
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4. Assessing cross-market correlations with numerical data.
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5. Analyzing institutional trends, providing investment amounts.
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Key focus areas:
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- Global economic indicators, including GDP growth rates and inflation.
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- Crypto market cycle analysis, detailing historical price movements.
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- Regulatory landscape changes, specifying impacts on the market.
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- Institutional adoption trends, quantifying investment flows.
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- Cross-asset correlations, providing correlation coefficients.
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Provide macro context and analysis of how broader trends affect the crypto market.
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Focus on identifying major market-moving factors and trends.
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"""
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macro_agent = Agent(
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agent_name="Macro-Analyst",
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system_prompt=MACRO_ANALYST_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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verbose=True,
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dynamic_temperature_enabled=True,
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)
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# Create group chat with all agents
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agents = [technical_agent, risk_agent, technical_agent]
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def fetch_htx_data(coin_name: str):
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base_url = "https://api.huobi.pro"
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# Fetch market ticker data for the coin
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ticker_endpoint = "/market/detail/merged"
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ticker_params = {
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"symbol": f"{coin_name.lower()}usdt"
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} # Assuming USDT pairing
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try:
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ticker_response = requests.get(
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base_url + ticker_endpoint, params=ticker_params
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)
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ticker_data = ticker_response.json()
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if ticker_data["status"] != "ok":
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return {
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"error": "Unable to fetch ticker data",
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"details": ticker_data,
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}
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# Fetch order book data for the coin
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order_book_endpoint = "/market/depth"
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order_book_params = {
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"symbol": f"{coin_name.lower()}usdt",
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"type": "step0",
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}
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order_book_response = requests.get(
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base_url + order_book_endpoint, params=order_book_params
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)
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order_book_data = order_book_response.json()
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if order_book_data["status"] != "ok":
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return {
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"error": "Unable to fetch order book data",
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"details": order_book_data,
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}
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# Fetch recent trades for the coin
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trades_endpoint = "/market/history/trade"
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trades_params = {
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"symbol": f"{coin_name.lower()}usdt",
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"size": 200,
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}
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trades_response = requests.get(
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base_url + trades_endpoint, params=trades_params
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)
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trades_data = trades_response.json()
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if trades_data["status"] != "ok":
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return {
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"error": "Unable to fetch trade data",
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"details": trades_data,
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}
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# Fetch Kline (Candlestick) data
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kline_endpoint = "/market/history/kline"
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kline_params = {
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"symbol": f"{coin_name.lower()}usdt",
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"period": "1day",
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"size": 200,
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}
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kline_response = requests.get(
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base_url + kline_endpoint, params=kline_params
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)
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kline_data = kline_response.json()
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if kline_data["status"] != "ok":
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return {
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"error": "Unable to fetch kline data",
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"details": kline_data,
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}
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# Format and prepare data for a single coin
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formatted_data = {
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"coin": coin_name.upper(),
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"ticker": {
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"current_price": ticker_data["tick"].get("close"),
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"high": ticker_data["tick"].get("high"),
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"low": ticker_data["tick"].get("low"),
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"open": ticker_data["tick"].get("open"),
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"volume": ticker_data["tick"].get("vol"),
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"amount": ticker_data["tick"].get("amount"),
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"count": ticker_data["tick"].get("count"),
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},
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"order_book": {
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"bids": [
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{"price": bid[0], "amount": bid[1]}
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for bid in order_book_data["tick"].get("bids", [])
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],
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"asks": [
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{"price": ask[0], "amount": ask[1]}
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for ask in order_book_data["tick"].get("asks", [])
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],
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},
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"recent_trades": [
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{
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"price": trade["data"][0].get("price"),
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"amount": trade["data"][0].get("amount"),
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"direction": trade["data"][0].get("direction"),
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"trade_id": trade["data"][0].get("id"),
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"timestamp": trade["data"][0].get("ts"),
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}
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for trade in trades_data.get("data", [])
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],
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"kline_data": [
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{
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"timestamp": kline["id"],
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"open": kline["open"],
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"close": kline["close"],
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"high": kline["high"],
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"low": kline["low"],
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"volume": kline["vol"],
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"amount": kline.get("amount"),
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}
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for kline in kline_data.get("data", [])
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],
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}
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return formatted_data
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except requests.exceptions.RequestException as e:
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return {"error": "HTTP request failed", "details": str(e)}
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data = fetch_htx_data("eth")
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swarm = SequentialWorkflow(
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name="htx-swarm",
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description="Swarm that analyzes data from HTX",
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agents=agents,
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max_loops=1,
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)
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out = swarm.run(
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f"Analyze the price action of the swarms coin over the past week. {str(data)} Conduct an analysis of the coin and provide a detailed report."
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)
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print(out)
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@ -1,63 +0,0 @@
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# 🚀 Latest Updates to Swarms - Twitter Thread
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🧵 Thread: Exciting new features in Swarms - the powerful AI agent framework! #AI #AGI #Development
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1/9 🤖 Introducing the ReasoningDuo - a revolutionary dual-agent system that combines reasoning and execution agents for more robust and reliable outputs! Perfect for complex problem-solving tasks.
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2/9 🔄 New Self-Consistency Agent with parallel processing:
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- Generates multiple independent responses
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- Uses ThreadPoolExecutor for concurrent execution
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- Aggregates results for higher accuracy
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#AI #ParallelProcessing
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3/9 🎯 The ReasoningAgentRouter is here! Dynamically select and execute different reasoning strategies:
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- ReasoningDuo
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- Self-Consistency
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- Iterative Reflective Expansion (IRE)
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#AIAgents
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4/9 💡 Advanced Reasoning Capabilities:
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- Structured problem analysis
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- Multiple solution exploration
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- Bias detection and transparency
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- Error handling strategies
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#ArtificialIntelligence
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5/9 ⚡️ Performance Improvements:
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- Concurrent response generation
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- Batched task processing
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- Optimized thread management
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- Improved error handling
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#Performance
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6/9 🎛️ Customization Options:
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- Adjustable sample sizes
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- Flexible model selection
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- Customizable system prompts
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- Multiple output formats
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#Flexibility
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7/9 🔍 Enhanced Evaluation Features:
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- Response validation
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- Answer checking
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- Majority voting system
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- Comprehensive logging
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#QualityAssurance
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8/9 📊 New Output Types:
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- Dictionary format
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- List format
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- Conversation history
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- Structured analysis
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#DataScience
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9/9 🌟 Coming Soon:
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- More agent types
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- Enhanced routing strategies
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- Advanced aggregation methods
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- Expanded model support
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Stay tuned! #FutureOfAI
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---
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Follow for more updates on Swarms! 🚀
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#AI #MachineLearning #AGI #Development
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