The post EUR/USD treads water with Dollar firmer and EZ inflation easing appeared on BitcoinEthereumNews.com. EUR/USD hovers around its Wednesday’s opening priceThe post EUR/USD treads water with Dollar firmer and EZ inflation easing appeared on BitcoinEthereumNews.com. EUR/USD hovers around its Wednesday’s opening price

EUR/USD treads water with Dollar firmer and EZ inflation easing

EUR/USD hovers around its Wednesday’s opening price at around 1.1750 virtually unchanged amid a scarce economic docket in the US that witnessed a Dollar recovery. Meanwhile, inflation figures from the Eurozone (EZ) and business confidence deterioration in Germany, kept the single currency pressured.

Single currency trades flat as softer Eurozone inflation and weak German sentiment offset dovish Fed rhetoric

In the US, Atlanta Fed President Raphael Bostic crossed the wires, saying that the he expects GDP growth is solid and that he expects the trend to continue in 2026. Earlier, Fed Governor Christopher Waller struck neutral to dovish comments, saying that he supports further easing the next year.

Ahead, the US docket will feature inflation figures and the US, and Initial Jobless Claims for the week ending December 13.

Across the pond, inflation in EZ dipped a relief for the European Central Bank (ECB) which hinted that the easing cycle was done. German’s IFO Business Confidence poll reported that sentiment deteriorated for the second straight month.

Traders’ eyes shift to ECB’s December monetary policy meeting, which is expected to be an event that would not move the needle, as President Christine Lagarde and Co., are expected to hold rates unchanged, for this meeting and for the whole next year.

In the meantime, the conflict between Russia and Ukraine could be a headwind for the Euro. The Ukrainian President Zelenskiy exerts pressure on Europe, saying that they should use Russia’s frozen assets to end Putin’s appetite for war.

Politico revealed that the US and Russia would hold talks over Ukraine war in Miami this weekend.

Euro Price This week

The table below shows the percentage change of Euro (EUR) against listed major currencies this week. Euro was the strongest against the Australian Dollar.

USDEURGBPJPYCADAUDNZDCHF
USD-0.03%0.02%-0.14%0.12%0.65%0.43%-0.09%
EUR0.03%0.07%-0.13%0.12%0.70%0.45%-0.06%
GBP-0.02%-0.07%-0.06%0.10%0.65%0.40%-0.10%
JPY0.14%0.13%0.06%0.25%0.81%0.56%0.29%
CAD-0.12%-0.12%-0.10%-0.25%0.55%0.31%-0.04%
AUD-0.65%-0.70%-0.65%-0.81%-0.55%-0.24%-0.75%
NZD-0.43%-0.45%-0.40%-0.56%-0.31%0.24%-0.51%
CHF0.09%0.06%0.10%-0.29%0.04%0.75%0.51%

The heat map shows percentage changes of major currencies against each other. The base currency is picked from the left column, while the quote currency is picked from the top row. For example, if you pick the Euro from the left column and move along the horizontal line to the US Dollar, the percentage change displayed in the box will represent EUR (base)/USD (quote).

Daily digest market movers: Euro steadies ahead of ECB’s meeting

  • Raphael Bostic said that although a close call, “inflation is more worrying than jobs.” He said that GDP growth is solid and that a stronger economy “will take pressure off the job market.”
  • Fed Governor Christopher Waller said that recent rate cuts have supported the labor market, noting that policy remains 50 to 100 basis points above neutral. However, he stressed that there is no urgency to deliver additional easing, adding that inflation is unlikely to reaccelerate.
  • The US Bureau of Labor Statistics (BLS) reported that Nonfarm Payrolls increased by 64K in November, topping forecasts of 50K and rebounding from October’s revised –105K decline. However, the Unemployment Rate rose to 4.6% from 4.4%, overshooting the Federal Reserve’s 4.5% projection.
  • Meanwhile, US Retail Sales stalled in October, unchanged on the month after a 0.1% gain in September and below expectations for a modest increase. In contrast, control-group sales, which feed directly into GDP calculations, rebounded sharply, rising 0.8% after a prior 0.1% contraction.

Technical outlook: EUR/USD remains bullish above 1.1700

EUR/USD consolidates in the mid-range of the 1.1700-1.1800 area as traders wait for the ECB’s decision. The Relative Strength Index (RSI) is bullish an indication that buyers are in control. But their lack of strength to clear 1.1800, would pave the way for further downside.

If EUR/USD clears 1.1800, expect a test of the 1.1850 region and, ultimately, the yearly high at 1.1918. Otherwise, the EUR/USD could drop below 1.1700, clearing the path to challenge the 100-day Simple Moving Average (SMA) near 1.1651, ahead of the 1.1600 handle.

EUR/USD daily chart

Euro FAQs

The Euro is the currency for the 20 European Union countries that belong to the Eurozone. It is the second most heavily traded currency in the world behind the US Dollar. In 2022, it accounted for 31% of all foreign exchange transactions, with an average daily turnover of over $2.2 trillion a day.
EUR/USD is the most heavily traded currency pair in the world, accounting for an estimated 30% off all transactions, followed by EUR/JPY (4%), EUR/GBP (3%) and EUR/AUD (2%).

The European Central Bank (ECB) in Frankfurt, Germany, is the reserve bank for the Eurozone. The ECB sets interest rates and manages monetary policy.
The ECB’s primary mandate is to maintain price stability, which means either controlling inflation or stimulating growth. Its primary tool is the raising or lowering of interest rates. Relatively high interest rates – or the expectation of higher rates – will usually benefit the Euro and vice versa.
The ECB Governing Council makes monetary policy decisions at meetings held eight times a year. Decisions are made by heads of the Eurozone national banks and six permanent members, including the President of the ECB, Christine Lagarde.

Eurozone inflation data, measured by the Harmonized Index of Consumer Prices (HICP), is an important econometric for the Euro. If inflation rises more than expected, especially if above the ECB’s 2% target, it obliges the ECB to raise interest rates to bring it back under control.
Relatively high interest rates compared to its counterparts will usually benefit the Euro, as it makes the region more attractive as a place for global investors to park their money.

Data releases gauge the health of the economy and can impact on the Euro. Indicators such as GDP, Manufacturing and Services PMIs, employment, and consumer sentiment surveys can all influence the direction of the single currency.
A strong economy is good for the Euro. Not only does it attract more foreign investment but it may encourage the ECB to put up interest rates, which will directly strengthen the Euro. Otherwise, if economic data is weak, the Euro is likely to fall.
Economic data for the four largest economies in the euro area (Germany, France, Italy and Spain) are especially significant, as they account for 75% of the Eurozone’s economy.

Another significant data release for the Euro is the Trade Balance. This indicator measures the difference between what a country earns from its exports and what it spends on imports over a given period.
If a country produces highly sought after exports then its currency will gain in value purely from the extra demand created from foreign buyers seeking to purchase these goods. Therefore, a positive net Trade Balance strengthens a currency and vice versa for a negative balance.

Source: https://www.fxstreet.com/news/eur-usd-treads-water-with-dollar-firmer-and-ez-inflation-easing-202512172246

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Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. 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Medium2025/09/18 14:40