Estimate Analysis

作者 himself6501fc7b4b34ae无许可证3.3K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库3天前更新

Analyze sell-side analyst estimates and how they are changing, using Yahoo Finance data (yfinance): EPS and revenue consensus by period, estimate ranges and dispersion, revision trends over 7/30/60/90 days and up/down revision breadth, growth estimates vs industry, sector, and the S&P 500, and historical estimate accuracy. Use this skill when the user wants more than a single estimate lookup: estimate revisions or momentum, EPS trend, consensus changes, forward or next-quarter and annual estimates, the bull vs bear estimate spread, or growth projections across periods.

AI 生成的概览

使用 yfinance 获取 Yahoo Finance 数据,分析卖方分析师的每股收益与营收预估及其修正趋势。

功能
通过 yfinance 从 Yahoo Finance 获取每股收益与营收的共识预估、预估区间、7/30/60/90 天的修正趋势、修正广度、相对行业、板块和标普 500 的增长预估,以及历史预估准确度。内容分为预估概览、修正趋势、修正广度、增长比较和历史意外情况等部分。产出为带表格的解读性文字分析,并附有说明:数据仅供研究与教育用途,不构成财务建议。
适用场景
当用户需要的不只是单次预估查询时使用,例如预估修正或动能、每股收益趋势、共识变化、未来或下一季度及年度预估、看涨与看跌预估差距,或跨周期的增长预测。适用于分析某公司分析师预期如何变化的问题。
运行要求
需要 Python 以及 yfinance 和 pandas;技能说明在未安装时通过 pip 安装 yfinance。需要访问 Yahoo Finance 数据的网络连接和一个股票代码。不附带脚本,只有说明文档和参考文件。

Estimate Analysis Skill

Deep-dives into analyst estimates and revision trends using Yahoo Finance data via yfinance. Covers EPS and revenue estimate distributions, revision momentum, growth projections, and multi-period comparisons — the full picture of where the street thinks a company is heading.

Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.


Step 1: Ensure yfinance Is Available

Current environment status:

!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`

If YFINANCE_NOT_INSTALLED, install it:

python
import subprocess, syssubprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])

If already installed, skip to the next step.


Step 2: Identify the Ticker and Gather Estimate Data

Extract the ticker from the user's request. Fetch all estimate-related data in one script.

python
import yfinance as yfimport pandas as pd
ticker = yf.Ticker("AAPL")  # replace with actual ticker
# --- Estimate data ---earnings_est = ticker.earnings_estimate      # EPS estimates by periodrevenue_est = ticker.revenue_estimate        # Revenue estimates by periodeps_trend = ticker.eps_trend                 # EPS estimate changes over timeeps_revisions = ticker.eps_revisions         # Up/down revision countsgrowth_est = ticker.growth_estimates         # Growth rate estimates
# --- Historical context ---earnings_hist = ticker.earnings_history      # Track recordinfo = ticker.info                           # Company basicsquarterly_income = ticker.quarterly_income_stmt  # Recent actuals

What each data source provides

Data SourceWhat It ShowsWhy It Matters
earnings_estimateCurrent EPS consensus by period (0q, +1q, 0y, +1y)The estimate levels — what analysts expect
revenue_estimateCurrent revenue consensus by periodTop-line expectations
eps_trendHow the EPS estimate has changed (7d, 30d, 60d, 90d ago)Revision direction — rising or falling expectations
eps_revisionsCount of upward vs downward revisions (7d, 30d)Revision breadth — are most analysts raising or cutting?
growth_estimatesGrowth rate estimates vs peers and sectorRelative positioning
earnings_historyActual vs estimated for last 4 quartersCalibration — how good are these estimates historically?

Step 3: Route Based on User Intent

Match the depth of the analysis to the question:

User RequestFocus AreaKey Sections
General estimate analysisFull analysisAll sections
"How have estimates changed"Revision trendsEPS Trend + Revisions
"What are analysts expecting"Current consensusEstimate overview
"Growth estimates"Growth projectionsGrowth Estimates
"Bull vs bear case"Estimate rangeHigh/low spread analysis
Compare estimates across periodsMulti-periodPeriod comparison table

A general request gets the full analysis; a narrow question gets the matching sections.


Step 4: Build the Estimate Analysis

Section 1: Estimate Overview

Present the current consensus for every available period (0q, +1q, 0y, +1y) from earnings_estimate and revenue_estimate: consensus, low, high, range width (as a % of consensus), analyst count, and YoY growth. Flag:

  • Range width — ranges wider than 15% of consensus signal high uncertainty
  • Analyst coverage — fewer than 5 analysts means thin coverage
  • Growth trajectory — whether growth accelerates or decelerates across periods

Section 2: Revision Trends (EPS Trend)

Often the most actionable section. From eps_trend, show each period's current estimate against its value 7, 30, 60, and 90 days ago, and summarize the direction and whether the recent moves are accelerating.

How to read it:

  • Rising estimates ahead of earnings = positive setup (the bar is rising)
  • Falling estimates = analysts cutting numbers, often a negative signal
  • Flat estimates = no new information being priced in
  • Recent acceleration or deceleration matters more than the total move

Section 3: Revision Breadth (EPS Revisions)

From eps_revisions, show up vs down revision counts over the last 7 and 30 days for each period, and the revision ratio Up / (Up + Down). Ratios above 0.7 are strongly bullish; below 0.3 are bearish.

Section 4: Growth Estimates

From growth_estimates, compare the company's expected growth for each period (and its past 5-year annual growth) with its industry, sector, and the S&P 500, and say whether it is expected to grow faster or slower than its peers.

Section 5: Historical Estimate Accuracy

From earnings_history, show estimate vs actual EPS and the surprise % for the last four quarters, then assess:

  • Beat rate — how many of the four quarters beat
  • Average surprise — magnitude and direction
  • Trend in surprise — are beats getting bigger or smaller? A shrinking surprise with rising estimates can mean the bar is catching up to reality.

Step 5: Synthesize and Respond

Lead with the key insight — the direction and breadth of revisions across periods — then show the tables for the sections the user cares about. Interpret rather than just tabulate: does the revision trend confirm or contradict the stock's recent price action, how does the growth outlook compare with what the current P/E prices in, and what does the estimate-accuracy history say about today's consensus?

Flag the nuances that apply: estimates cluster around consensus, so the real distribution of outcomes is wider than low/high suggests; revision momentum can reverse on a single guidance change or macro event; Yahoo Finance estimates can lag real-time consensus providers by hours or days; out-year (+1y) estimates are inherently less reliable. Close with the standing caveats: analyst estimates reflect a consensus view, not certainty; revisions are a signal, not a guarantee; this is not financial advice.


Reference Files

  • references/api_reference.md — Detailed yfinance API reference for all estimate-related methods

Read the reference file when you need exact return formats or edge case handling.

来源与署名

来源:himself65/finance-skills位于plugins/market-analysis/skills/estimate-analysis提交01fc7b4

许可证: 无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

更多来自 himself65/finance-skills 的技能

Yfinance Data

himself65

通过 yfinance Python 库从 Yahoo Finance 获取股票与市场数据并加以呈现。

Data & Analytics3.3K3天前更新

Stock Liquidity

himself65

Analyze how liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about liquidity or trading costs — how easily a position can be entered or exited, what a large order would do to the price, spread or execution-cost estimates, order book depth, volume patterns, or liquidity comparisons — especially for small caps, penny stocks, and thinly traded names.

待分类3.3K3天前更新

Stock Correlation

himself65

基于 Yahoo Finance 价格历史分析股票联动,找出相关个股、贝塔、聚类与滚动相关性。

Data & Analytics3.3K3天前更新

Sepa Strategy

himself65

使用 Mark Minervini 的 SEPA 方法分析股票:阶段分析、趋势模板、形态、入场点与仓位管理。

Business & Finance3.3K3天前更新

Saas Valuation Compression

himself65

分析私营 SaaS 公司各融资轮次的 ARR 估值倍数变化,并将变化归因于宏观、增长与叙事因素。

Business & Finance3.3K3天前更新

Yc Reader

himself65

Look up Y Combinator companies and batches from the public yc-oss API, a static JSON dataset refreshed daily: company profiles, batch rosters (e.g. W25, S24), companies by industry or tag, top companies, who is hiring, founder-diversity lists, and overall YC stats. Use this skill whenever the user asks about YC-backed startups, a YC batch, YC companies in a sector or tag or that are hiring, the Y Combinator portfolio, or startup and venture research that draws on YC data. Read-only.

待分类3.3K3天前更新