Kye Gomez
3 weeks ago
parent
246ea8cf75
commit
1008c9352e
@ -1,554 +0,0 @@
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from typing import Dict, List
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from datetime import datetime
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from loguru import logger
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from swarms.structs.tree_swarm import TreeAgent, Tree, ForestSwarm
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import asyncio
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import json
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import aiohttp
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from bs4 import BeautifulSoup
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import xml.etree.ElementTree as ET
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# Configure logging
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logger.add("forex_forest.log", rotation="500 MB", level="INFO")
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class ForexDataFeed:
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"""Real-time forex data collector using free open sources"""
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def __init__(self):
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self.pairs = [
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"EUR/USD",
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"GBP/USD",
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"USD/JPY",
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"AUD/USD",
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"USD/CAD",
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]
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async def fetch_ecb_rates(self) -> Dict:
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"""Fetch exchange rates from European Central Bank (no key required)"""
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try:
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url = "https://www.ecb.europa.eu/stats/eurofxref/eurofxref-daily.xml"
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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xml_data = await response.text()
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root = ET.fromstring(xml_data)
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rates = {}
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for cube in root.findall(".//*[@currency]"):
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currency = cube.get("currency")
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rate = float(cube.get("rate"))
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rates[currency] = rate
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# Calculate cross rates
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rates["EUR"] = 1.0 # Base currency
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cross_rates = {}
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for pair in self.pairs:
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base, quote = pair.split("/")
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if base in rates and quote in rates:
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cross_rates[pair] = rates[base] / rates[quote]
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return cross_rates
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except Exception as e:
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logger.error(f"Error fetching ECB rates: {e}")
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return {}
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async def fetch_forex_factory_data(self) -> Dict:
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"""Scrape trading data from Forex Factory"""
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try:
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url = "https://www.forexfactory.com"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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}
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async with aiohttp.ClientSession() as session:
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async with session.get(
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url, headers=headers
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) as response:
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text = await response.text()
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soup = BeautifulSoup(text, "html.parser")
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# Get calendar events
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calendar = []
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calendar_table = soup.find(
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"table", class_="calendar__table"
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)
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if calendar_table:
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for row in calendar_table.find_all(
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"tr", class_="calendar__row"
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):
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try:
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event = {
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"currency": row.find(
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"td", class_="calendar__currency"
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).text.strip(),
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"event": row.find(
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"td", class_="calendar__event"
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).text.strip(),
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"impact": row.find(
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"td", class_="calendar__impact"
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).text.strip(),
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"time": row.find(
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"td", class_="calendar__time"
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).text.strip(),
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}
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calendar.append(event)
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except:
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continue
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return {"calendar": calendar}
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except Exception as e:
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logger.error(f"Error fetching Forex Factory data: {e}")
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return {}
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async def fetch_tradingeconomics_data(self) -> Dict:
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"""Scrape economic data from Trading Economics"""
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try:
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url = "https://tradingeconomics.com/calendar"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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}
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async with aiohttp.ClientSession() as session:
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async with session.get(
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url, headers=headers
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) as response:
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text = await response.text()
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soup = BeautifulSoup(text, "html.parser")
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# Get economic indicators
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indicators = []
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calendar_table = soup.find("table", class_="table")
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if calendar_table:
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for row in calendar_table.find_all("tr")[
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1:
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]: # Skip header
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try:
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cols = row.find_all("td")
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indicator = {
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"country": cols[0].text.strip(),
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"indicator": cols[1].text.strip(),
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"actual": cols[2].text.strip(),
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"previous": cols[3].text.strip(),
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"consensus": cols[4].text.strip(),
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}
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indicators.append(indicator)
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except:
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continue
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return {"indicators": indicators}
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except Exception as e:
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logger.error(
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f"Error fetching Trading Economics data: {e}"
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)
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return {}
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async def fetch_dailyfx_data(self) -> Dict:
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"""Scrape market analysis from DailyFX"""
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try:
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url = "https://www.dailyfx.com/market-news"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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}
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async with aiohttp.ClientSession() as session:
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async with session.get(
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url, headers=headers
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) as response:
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text = await response.text()
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soup = BeautifulSoup(text, "html.parser")
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# Get market news and analysis
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news = []
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articles = soup.find_all("article", class_="dfx-article")
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for article in articles[:10]: # Get latest 10 articles
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try:
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news_item = {
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"title": article.find("h3").text.strip(),
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"summary": article.find("p").text.strip(),
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"currency": article.get(
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"data-currency", "General"
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),
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"timestamp": article.find("time").get(
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"datetime"
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),
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}
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news.append(news_item)
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except:
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continue
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return {"news": news}
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except Exception as e:
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logger.error(f"Error fetching DailyFX data: {e}")
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return {}
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async def fetch_all_data(self) -> Dict:
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"""Fetch and combine all forex data sources"""
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try:
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# Fetch data from all sources concurrently
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rates, ff_data, te_data, dx_data = await asyncio.gather(
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self.fetch_ecb_rates(),
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self.fetch_forex_factory_data(),
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self.fetch_tradingeconomics_data(),
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self.fetch_dailyfx_data(),
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)
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# Combine all data
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market_data = {
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"exchange_rates": rates,
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"calendar": ff_data.get("calendar", []),
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"economic_indicators": te_data.get("indicators", []),
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"market_news": dx_data.get("news", []),
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"timestamp": datetime.now().isoformat(),
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}
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return market_data
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except Exception as e:
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logger.error(f"Error fetching all data: {e}")
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return {}
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# Rest of the ForexForestSystem class remains the same...
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# (Previous ForexDataFeed class code remains the same...)
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# Specialized Agent Prompts
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TECHNICAL_ANALYST_PROMPT = """You are an expert forex technical analyst agent.
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Your responsibilities:
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1. Analyze real-time exchange rate data for patterns and trends
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2. Calculate cross-rates and currency correlations
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3. Generate trading signals based on price action
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4. Monitor market volatility and momentum
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5. Identify key support and resistance levels
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Data Format:
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- You will receive exchange rates from ECB and calculated cross-rates
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- Focus on major currency pairs and their relationships
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- Consider market volatility and trading volumes
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Output Format:
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{
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"analysis_type": "technical",
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"timestamp": "ISO timestamp",
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"signals": [
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{
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"pair": "Currency pair",
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"trend": "bullish/bearish/neutral",
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"strength": 1-10,
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"key_levels": {"support": [], "resistance": []},
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"recommendation": "buy/sell/hold"
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}
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]
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}"""
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FUNDAMENTAL_ANALYST_PROMPT = """You are an expert forex fundamental analyst agent.
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Your responsibilities:
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1. Analyze economic calendar events and their impact
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2. Evaluate economic indicators from Trading Economics
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3. Assess market news and sentiment from DailyFX
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4. Monitor central bank actions and policies
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5. Track geopolitical events affecting currencies
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Data Format:
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- Economic calendar events with impact levels
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- Latest economic indicators and previous values
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- Market news and analysis from reliable sources
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- Central bank statements and policy changes
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Output Format:
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{
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"analysis_type": "fundamental",
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"timestamp": "ISO timestamp",
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"assessments": [
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{
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"currency": "Currency code",
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"economic_outlook": "positive/negative/neutral",
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"key_events": [],
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"impact_score": 1-10,
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"bias": "bullish/bearish/neutral"
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}
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]
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}"""
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MARKET_SENTIMENT_PROMPT = """You are an expert market sentiment analysis agent.
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Your responsibilities:
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1. Analyze news sentiment from DailyFX articles
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2. Track market positioning and bias
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3. Monitor risk sentiment and market fear/greed
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4. Identify potential market drivers
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5. Detect sentiment shifts and extremes
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Data Format:
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- Market news and analysis articles
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- Trading sentiment indicators
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- Risk event calendar
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- Market commentary and analysis
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Output Format:
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{
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"analysis_type": "sentiment",
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"timestamp": "ISO timestamp",
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"sentiment_data": [
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{
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"pair": "Currency pair",
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"sentiment": "risk-on/risk-off",
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"strength": 1-10,
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"key_drivers": [],
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"outlook": "positive/negative/neutral"
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}
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]
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}"""
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STRATEGY_COORDINATOR_PROMPT = """You are the lead forex strategy coordination agent.
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Your responsibilities:
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1. Synthesize technical, fundamental, and sentiment analysis
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2. Generate final trading recommendations
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3. Manage risk exposure and position sizing
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4. Coordinate entry and exit points
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5. Monitor open positions and adjust strategies
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Data Format:
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- Analysis from technical, fundamental, and sentiment agents
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- Current market rates and conditions
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- Economic calendar and news events
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- Risk parameters and exposure limits
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Output Format:
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{
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"analysis_type": "strategy",
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"timestamp": "ISO timestamp",
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"recommendations": [
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{
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"pair": "Currency pair",
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"action": "buy/sell/hold",
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"confidence": 1-10,
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"entry_points": [],
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"stop_loss": float,
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"take_profit": float,
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"rationale": "string"
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}
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]
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}"""
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class ForexForestSystem:
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"""Main system coordinating the forest swarm and data feeds"""
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def __init__(self):
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"""Initialize the forex forest system"""
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self.data_feed = ForexDataFeed()
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# Create Technical Analysis Tree
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technical_agents = [
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TreeAgent(
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system_prompt=TECHNICAL_ANALYST_PROMPT,
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agent_name="Price Action Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=TECHNICAL_ANALYST_PROMPT,
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agent_name="Cross Rate Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=TECHNICAL_ANALYST_PROMPT,
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agent_name="Volatility Analyst",
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model_name="gpt-4o",
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),
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]
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# Create Fundamental Analysis Tree
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fundamental_agents = [
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TreeAgent(
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system_prompt=FUNDAMENTAL_ANALYST_PROMPT,
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agent_name="Economic Data Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=FUNDAMENTAL_ANALYST_PROMPT,
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agent_name="News Impact Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=FUNDAMENTAL_ANALYST_PROMPT,
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agent_name="Central Bank Analyst",
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model_name="gpt-4o",
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),
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]
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# Create Sentiment Analysis Tree
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sentiment_agents = [
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TreeAgent(
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system_prompt=MARKET_SENTIMENT_PROMPT,
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agent_name="News Sentiment Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=MARKET_SENTIMENT_PROMPT,
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agent_name="Risk Sentiment Analyst",
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model_name="gpt-4o",
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),
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TreeAgent(
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system_prompt=MARKET_SENTIMENT_PROMPT,
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agent_name="Market Positioning Analyst",
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model_name="gpt-4o",
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),
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]
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# Create Strategy Coordination Tree
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strategy_agents = [
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TreeAgent(
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system_prompt=STRATEGY_COORDINATOR_PROMPT,
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agent_name="Lead Strategy Coordinator",
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model_name="gpt-4",
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temperature=0.5,
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),
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TreeAgent(
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system_prompt=STRATEGY_COORDINATOR_PROMPT,
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agent_name="Risk Manager",
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model_name="gpt-4",
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temperature=0.5,
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),
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TreeAgent(
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system_prompt=STRATEGY_COORDINATOR_PROMPT,
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agent_name="Position Manager",
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model_name="gpt-4",
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temperature=0.5,
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),
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]
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# Create trees
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self.technical_tree = Tree(
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tree_name="Technical Analysis", agents=technical_agents
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)
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self.fundamental_tree = Tree(
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tree_name="Fundamental Analysis",
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agents=fundamental_agents,
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)
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self.sentiment_tree = Tree(
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tree_name="Sentiment Analysis", agents=sentiment_agents
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)
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self.strategy_tree = Tree(
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tree_name="Strategy Coordination", agents=strategy_agents
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)
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# Create forest swarm
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self.forest = ForestSwarm(
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trees=[
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self.technical_tree,
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self.fundamental_tree,
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self.sentiment_tree,
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self.strategy_tree,
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]
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)
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logger.info("Forex Forest System initialized successfully")
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async def prepare_analysis_task(self) -> str:
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"""Prepare the analysis task with real-time data"""
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try:
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market_data = await self.data_feed.fetch_all_data()
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task = {
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"action": "analyze_forex_markets",
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"market_data": market_data,
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"timestamp": datetime.now().isoformat(),
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"analysis_required": [
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"technical",
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"fundamental",
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"sentiment",
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"strategy",
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],
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}
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return json.dumps(task, indent=2)
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except Exception as e:
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logger.error(f"Error preparing analysis task: {e}")
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raise
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async def run_analysis_cycle(self) -> Dict:
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"""Run a complete analysis cycle with the forest swarm"""
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try:
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# Prepare task with real-time data
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task = await self.prepare_analysis_task()
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# Run forest swarm analysis
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result = self.forest.run(task)
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# Parse and validate results
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analysis = (
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json.loads(result)
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if isinstance(result, str)
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else result
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)
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logger.info("Analysis cycle completed successfully")
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return analysis
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except Exception as e:
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logger.error(f"Error in analysis cycle: {e}")
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raise
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async def monitor_markets(self, interval_seconds: int = 300):
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"""Continuously monitor markets and run analysis"""
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while True:
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try:
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# Run analysis cycle
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analysis = await self.run_analysis_cycle()
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# Log results
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logger.info("Market analysis completed")
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logger.debug(
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f"Analysis results: {json.dumps(analysis, indent=2)}"
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)
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# Process any trading signals
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if "recommendations" in analysis:
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await self.process_trading_signals(
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analysis["recommendations"]
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)
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# Wait for next interval
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await asyncio.sleep(interval_seconds)
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except Exception as e:
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logger.error(f"Error in market monitoring: {e}")
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await asyncio.sleep(60)
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async def process_trading_signals(
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self, recommendations: List[Dict]
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):
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"""Process and log trading signals from analysis"""
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try:
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for rec in recommendations:
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logger.info(
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f"Trading Signal: {rec['pair']} - {rec['action']}"
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)
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logger.info(f"Confidence: {rec['confidence']}/10")
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logger.info(f"Entry Points: {rec['entry_points']}")
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logger.info(f"Stop Loss: {rec['stop_loss']}")
|
||||
logger.info(f"Take Profit: {rec['take_profit']}")
|
||||
logger.info(f"Rationale: {rec['rationale']}")
|
||||
logger.info("-" * 50)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing trading signals: {e}")
|
||||
|
||||
|
||||
# Example usage
|
||||
async def main():
|
||||
"""Main function to run the Forex Forest System"""
|
||||
try:
|
||||
system = ForexForestSystem()
|
||||
await system.monitor_markets()
|
||||
except Exception as e:
|
||||
logger.error(f"Error in main: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Set up asyncio event loop and run the system
|
||||
asyncio.run(main())
|
||||
Loading…
Reference in new issue