# Daily Market Briefing Template

## Usage
This template defines the output format for daily market briefings. Fill in data from news collection and broker MCP tools.

---

## 📊 Market Briefing — [Day], [Date] [Month] [Year]

**Market Status:** [Open / Closed] | **Time:** [HH:MM IST]

---

### 🏦 Market Snapshot

| Index | LTP | Change | % Change | Trend |
|-------|-----|--------|----------|-------|
| Nifty 50 | [value] | [+/-value] | [+/-x.xx%] | [🟢/🔴] |
| Sensex | [value] | [+/-value] | [+/-x.xx%] | [🟢/🔴] |
| Bank Nifty | [value] | [+/-value] | [+/-x.xx%] | [🟢/🔴] |
| Nifty IT | [value] | [+/-value] | [+/-x.xx%] | [🟢/🔴] |
| India VIX | [value] | [+/-value] | [+/-x.xx%] | [🟢/🔴] |

**Market Breadth:** [Advances]: [Declines]: [Unchanged] | **A/D Ratio:** [x.xx]

---

### 📰 Top Stories (Impact Score ≥ 5)

#### 1. [Headline] — Impact: [X/10] [🟢/🔴/🟡]
- **Source:** [Source Name] | [Time]
- **Category:** [Event Classification]
- **Stocks Affected:** [SYMBOL1] ([+/-x%]), [SYMBOL2] ([+/-x%])
- **Key Detail:** [1-2 sentence summary of the news and its significance]

#### 2. [Headline] — Impact: [X/10] [🟢/🔴/🟡]
- **Source:** [Source Name] | [Time]
- **Category:** [Event Classification]
- **Stocks Affected:** [SYMBOL1] ([+/-x%]), [SYMBOL2] ([+/-x%])
- **Key Detail:** [1-2 sentence summary]

#### 3. [Headline] — Impact: [X/10] [🟢/🔴/🟡]
[Same format]

*(Continue for all stories with impact ≥ 5)*

---

### 🔥 Stocks in Focus

| Stock | LTP (Rs.) | Change | Volume vs Avg | News Driver |
|-------|-----------|--------|--------------|-------------|
| [SYMBOL] | [value] | [+/-x%] | [x.x]x | [Brief reason] |
| [SYMBOL] | [value] | [+/-x%] | [x.x]x | [Brief reason] |
| [SYMBOL] | [value] | [+/-x%] | [x.x]x | [Brief reason] |
| [SYMBOL] | [value] | [+/-x%] | [x.x]x | [Brief reason] |
| [SYMBOL] | [value] | [+/-x%] | [x.x]x | [Brief reason] |

---

### 📊 Institutional Activity

| Participant | Buy (Rs. Cr) | Sell (Rs. Cr) | Net (Rs. Cr) | Trend |
|------------|-------------|--------------|-------------|-------|
| FII/FPI | [value] | [value] | [+/-value] | [🟢/🔴] |
| DII | [value] | [value] | [+/-value] | [🟢/🔴] |

**FII Derivatives:** [Long/Short ratio], [Change from previous day]

---

### 📅 Upcoming Events

**Today / This Week:**
- [ ] [Company] — Q[x] FY[xx] results ([Date])
- [ ] [Company] — Board meeting ([Date], [Purpose])
- [ ] [Company] — Ex-dividend Rs.[amount] ([Date])
- [ ] [Event] — [Description] ([Date])

**Earnings Calendar (Next 5 Trading Days):**

| Date | Company | Event | Quarter |
|------|---------|-------|---------|
| [Date] | [Company] | Results | Q[x] FY[xx] |
| [Date] | [Company] | Results | Q[x] FY[xx] |

---

### 🏢 Corporate Actions (Coming Week)

| Company | Action | Details | Ex-Date | Record Date |
|---------|--------|---------|---------|-------------|
| [Company] | Dividend | Rs.[amount] per share | [Date] | [Date] |
| [Company] | Bonus | [ratio] | [Date] | [Date] |
| [Company] | Split | FV Rs.[from] → Rs.[to] | [Date] | [Date] |

---

### 🔔 Regulatory Updates

- [SEBI/RBI circular summary, if any]
- [Policy change summary, if any]
- [F&O ban list changes, if any]

---

### 🌍 Global Cues

| Market | Level | Change | Signal |
|--------|-------|--------|--------|
| US (S&P 500) | [value] | [+/-x%] | [🟢/🔴] |
| US (Nasdaq) | [value] | [+/-x%] | [🟢/🔴] |
| SGX Nifty | [value] | [+/-x%] | [🟢/🔴] |
| Crude Oil (Brent) | $[value] | [+/-x%] | [🟢/🔴] |
| USD/INR | [value] | [+/-x%] | [🟢/🔴] |
| Gold (MCX) | Rs.[value] | [+/-x%] | [🟢/🔴] |
| US 10Y Yield | [value]% | [+/-bps] | [🟢/🔴] |

---

### 📝 Key Takeaways

1. **[Main theme]** — [1 sentence summary]
2. **[Secondary theme]** — [1 sentence summary]
3. **[Watch item]** — [1 sentence on what to monitor]

---

### ⚠️ Disclaimer

This briefing is for educational and informational purposes only. It does not constitute investment advice. Please consult a SEBI-registered advisor before making investment decisions.

*Generated at [HH:MM IST] on [Date] using India News Tracker skill*

# Indian Financial News Source Guide

## Source Reliability & Speed Matrix

| Source | Reliability | Speed | Bias | Best For |
|--------|------------|-------|------|----------|
| BSE India (bseindia.com) | ★★★★★ | Medium | None (official) | Corporate filings, results, announcements |
| NSE India (nseindia.com) | ★★★★★ | Medium | None (official) | Bulk/block deals, F&O data, circulars |
| SEBI (sebi.gov.in) | ★★★★★ | Slow | None (regulatory) | Regulations, enforcement, circulars |
| RBI (rbi.org.in) | ★★★★★ | Slow | None (regulatory) | Monetary policy, banking norms, forex |
| MoneyControl | ★★★★ | Fast | Slight bullish | Breaking news, earnings, market updates |
| Economic Times | ★★★★ | Fast | Neutral | Corporate news, policy, deep dives |
| LiveMint | ★★★★ | Medium | Neutral | Premium analysis, policy, macro |
| Business Standard | ★★★★ | Medium | Neutral | Corporate, banking, policy |
| NDTV Profit | ★★★ | Fast | Neutral | Quick market updates, interviews |
| Trendlyne | ★★★★ | Medium | Data-driven | Bulk deals, technicals, earnings calendar |
| Screener.in | ★★★★ | Slow | Data-driven | Financials, screening, results |
| Financial Express | ★★★ | Medium | Neutral | Economy, policy, regulation |
| Reuters India | ★★★★★ | Fast | Neutral | Global macro, major corporate events |
| Bloomberg Quint | ★★★★ | Fast | Neutral | Premium analysis, global context |

## Optimal Search Patterns

### Breaking News (Last 24 hours)
```
"Indian stock market news today"
"NSE BSE market update [date]"
"site:moneycontrol.com markets today"
"site:economictimes.indiatimes.com stock market [date]"
```

### Company-Specific News
```
"[Company Name] NSE stock news [month] [year]"
"site:moneycontrol.com [Company Name]"
"site:bseindia.com [BSE Code] announcement"
"[Company Name] quarterly results [quarter] FY[year]"
"[Company Name] board meeting outcome"
```

### Sector News
```
"[Sector] sector India stock market [month] [year]"
"Nifty [Sector Index] news analysis"
"[Sector] policy regulation India [year]"
```

### Regulatory Updates
```
"SEBI circular [month] [year]"
"SEBI new regulation [year]"
"RBI monetary policy [month] [year]"
"RBI circular banking [year]"
```

### Institutional Activity
```
"FII DII data [date] NSE"
"bulk deals NSE today [date]"
"block deals BSE today [date]"
"promoter buying selling [month] [year]"
"mutual fund portfolio changes [quarter] [year]"
```

### Earnings & Results
```
"quarterly results schedule NSE [month] [year]"
"[Company] Q[x] FY[xx] results"
"earnings surprise India [quarter] [year]"
"results calendar upcoming NSE BSE"
```

### IPO News
```
"upcoming IPO India [month] [year]"
"IPO subscription status [company]"
"IPO listing price [company]"
"DRHP filing SEBI [year]"
```

### Corporate Actions
```
"dividend ex-date NSE [month] [year]"
"stock split bonus issue NSE [year]"
"buyback offer India [year]"
"rights issue NSE [year]"
```

## Source-Specific Parsing Notes

### MoneyControl
- URL pattern: `moneycontrol.com/news/business/markets/`
- Earnings URL: `moneycontrol.com/news/business/earnings/`
- Has dedicated earnings calendar section
- Flash news updates are usually reliable and fast
- Best for: Real-time market commentary, earnings analysis

### Economic Times Markets
- URL pattern: `economictimes.indiatimes.com/markets/stocks/`
- Has good ETF and MF coverage
- Policy analysis is particularly strong
- Best for: Deep corporate stories, policy impact analysis

### BSE India Filings
- URL pattern: `bseindia.com/corporates/ann.html`
- Search by scrip code or company name
- Categories: Board Meeting, Financial Results, Corporate Action, Shareholding
- Best for: Official corporate announcements (primary source of truth)

### NSE India
- Bulk deals: `nseindia.com/market-data/bulk-deal-data`
- Block deals: `nseindia.com/market-data/block-deal-data`
- Insider trades: `nseindia.com/companies-listing/corporate-filings-insider-trading`
- F&O ban: `nseindia.com/market-data/fno-ban`
- Best for: Official market data, institutional activity

### Trendlyne
- URL pattern: `trendlyne.com/stock-deals/`
- Has excellent bulk/block deal aggregation
- Earnings calendar with estimates
- Best for: Data-driven news, aggregated institutional activity

## Handling Conflicting Reports

When different sources report conflicting information:

1. **Prioritize official sources** (BSE/NSE filings > media reports)
2. **Check the timestamp** — newer information may supersede older
3. **Verify with 2+ independent sources** before treating as confirmed
4. **Label unconfirmed news** clearly: "Reports suggest..." or "According to [source]..."
5. **Flag rumor vs confirmed**: Clearly distinguish between market rumors and official announcements

## Common Misinformation Patterns

Watch out for:
- **Pump-and-dump social media posts** — verify any "breaking news" from X/Twitter against official sources
- **Misquoted earnings numbers** — always verify against BSE/NSE filing
- **Outdated news recycled** — check dates carefully, especially for regulatory changes
- **Promotional "news"** — some outlets publish sponsored content as news
- **Analyst estimates presented as results** — distinguish between expected and actual numbers

## News Timing Context

| Timing | Impact Pattern |
|--------|---------------|
| **Pre-market (before 9:15 AM)** | Creates gap up/down at open, look for global cues |
| **During market (9:15-3:30)** | Immediate price reaction, watch volume spike |
| **Post-market (after 3:30 PM)** | Impact priced in next day's open |
| **Weekend/Holiday** | Accumulates for Monday/post-holiday opening |
| **After F&O expiry** | May have outsized impact in next series |
| **Before results** | Rumor-driven, verify after official announcement |

# Indian Market Sector Mapping

## NSE Sectoral Indices → Key Stocks

### Banking & Financial Services

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Bank** | HDFCBANK, ICICIBANK, SBIN, KOTAKBANK, AXISBANK, INDUSINDBK, BANDHANBNK, FEDERALBNK, IDFCFIRSTB, AUBANK |
| **Nifty PSU Bank** | SBIN, BANKBARODA, PNB, CANBK, UNIONBANK, IOB, INDIANB, BANKINDIA, CENTRALBK, MAHABANK |
| **Nifty Private Bank** | HDFCBANK, ICICIBANK, KOTAKBANK, AXISBANK, INDUSINDBK, FEDERALBNK, BANDHANBNK, IDFCFIRSTB, RBLBANK |
| **Nifty Financial Services** | HDFCBANK, ICICIBANK, SBIN, KOTAKBANK, BAJFINANCE, BAJAJFINSV, AXISBANK, HDFCAMC, SBILIFE, ICICIGI |

**Key news categories:** RBI policy, NPA norms, credit growth, NIM trends, deposit rates, digital lending rules, NBFC regulations

### Information Technology

| Index | Top Constituents |
|-------|-----------------|
| **Nifty IT** | TCS, INFY, HCLTECH, WIPRO, TECHM, LTIM, MPHASIS, COFORGE, PERSISTENT, LTTS |

**Key news categories:** Deal wins, US IT spending, visa policies, INR/USD movement, margin guidance, attrition, AI impact

### Pharmaceuticals & Healthcare

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Pharma** | SUNPHARMA, DRREDDY, CIPLA, DIVISLAB, APOLLOHOSP, TORNTPHARM, LUPIN, AUROPHARMA, ALKEM, BIOCON |
| **Nifty Healthcare** | SUNPHARMA, DRREDDY, CIPLA, APOLLOHOSP, MAXHEALTH, FORTIS, LALPATHLAB, METROPOLIS, IPCALAB |

**Key news categories:** USFDA approvals/warnings, ANDA filings, API pricing, drug price control (DPCO), patent expiry, clinical trials

### Automobile

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Auto** | M&M, TATAMOTORS, MARUTI, BAJAJ-AUTO, HEROMOTOCO, EICHERMOT, BHARATFORG, ASHOKLEY, TVS, BOSCH |

**Key news categories:** Monthly sales data, EV policy, PLI scheme, commodity input costs (steel, rubber), export data, BSVI norms

### Fast Moving Consumer Goods

| Index | Top Constituents |
|-------|-----------------|
| **Nifty FMCG** | HINDUNILVR, ITC, NESTLEIND, BRITANNIA, GODREJCP, DABUR, MARICO, COLPAL, TATACONSUM, PGHH |

**Key news categories:** Rural demand, monsoon impact, inflation/deflation, raw material costs (palm oil, crude), GST rate changes

### Energy & Oil

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Energy** | RELIANCE, NTPC, POWERGRID, ONGC, ADANIGREEN, BPCL, IOC, GAIL, TATAPOWER, COALINDIA |
| **Nifty Oil & Gas** | RELIANCE, ONGC, BPCL, IOC, GAIL, HINDPETRO, PETRONET, GUJGASLTD, ATGL, IGL |

**Key news categories:** Crude oil prices, OPEC decisions, natural gas prices, refining margins, fuel price revisions, green energy policy, coal supply

### Metals & Mining

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Metal** | TATASTEEL, JSWSTEEL, HINDALCO, VEDL, COALINDIA, NMDC, ADANIENT, NATIONALUM, SAIL, JINDALSTEL |

**Key news categories:** Global metal prices, China demand, US tariffs, anti-dumping duty, iron ore prices, PLI for steel

### Real Estate

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Realty** | DLF, GODREJPROP, OBEROIRLTY, PRESTIGE, PHOENIXLTD, LODHA, BRIGADE, SOBHA, SUNTECK |

**Key news categories:** Interest rate changes, RERA compliance, housing demand, commercial real estate, REIT performance, circle rates

### Infrastructure & Construction

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Infra** | LARSEN, RELIANCE, NTPC, POWERGRID, ADANIPORTS, ULTRACEMCO, GRASIM, BHARTIARTL, TECHM, SBIN |

**Key news categories:** Government capex, highway contracts, railway orders, smart city projects, budget allocation, order inflows

### Telecom

**Key Stocks:** BHARTIARTL, IDEA, RCOM, TATACOMM, INDUSTOWER

**Key news categories:** Tariff hikes, spectrum auction, 5G rollout, ARPU trends, subscriber data, tower tenancy, AGR dues

### Power & Utilities

| Index | Top Constituents |
|-------|-----------------|
| **Nifty Power** | NTPC, POWERGRID, TATAPOWER, ADANIGREEN, JSW ENERGY, NHPC, SJVN, CESC |

**Key news categories:** Electricity demand, renewable energy targets, coal supply, power purchase agreements, transmission projects

### Consumer Durables

**Key Stocks:** VOLTAS, HAVELLS, BLUESTAR, DIXON, AMBER, WHIRLPOOL, CROMPTON, VIP

**Key news categories:** Festive demand, summer demand (ACs), BIS standards, PLI electronics, raw material costs

### Chemicals

**Key Stocks:** PIDILITIND, SRF, AARTI, DEEPAKNITRITE, CLEAN, ALKYLAMINE, GALAXYSURF

**Key news categories:** China+1 beneficiaries, agrochemical demand, specialty chemicals growth, environmental regulations, anti-dumping

## Sector → Event Sensitivity Map

| Event Type | Most Sensitive Sectors |
|------------|----------------------|
| **RBI rate cut** | Banks, Realty, Auto, NBFCs |
| **RBI rate hike** | Banks (short-term positive NIM), Realty (negative), NBFCs |
| **Crude oil spike** | OMCs (negative), Airlines (negative), Paints (negative), ONGC (positive) |
| **Crude oil drop** | OMCs (positive), Airlines (positive), Paints (positive), ONGC (negative) |
| **INR depreciation** | IT (positive), Pharma exports (positive), importers (negative) |
| **INR appreciation** | IT (negative), Pharma (negative), importers (positive) |
| **Monsoon good** | FMCG, Auto (rural), Agri, Fertilizers |
| **Monsoon poor** | FMCG (negative rural), Sugar, Agri inputs |
| **Union Budget** | Depends on allocations — Infra, Defense, Railways, PSU banks |
| **Global recession** | IT (negative), Metals (negative), Pharma (defensive) |
| **China slowdown** | Metals (negative), Chemicals (positive — China+1), Textiles (positive) |
| **US tariffs** | IT services, Pharma (if targeted), Metals |
| **Election results** | PSU stocks, Infra, Defense, Realty |
| **GST rate change** | Affected sector directly, FMCG, Consumer Durables |
| **SEBI F&O rules** | Brokers, exchanges, high-beta stocks |
| **PLI scheme** | Eligible sectors — Electronics, Pharma, Auto components, Solar |
| **Inflation spike** | FMCG (margin pressure), Banking (rate hike expectations) |
| **FII selling** | High-FII-holding stocks, large caps first, then mid/small |
| **DII buying** | Banks, IT, FMCG (defensive accumulation patterns) |

## Sector Seasonality Patterns

| Month | Seasonal Theme | Sectors Affected |
|-------|---------------|-----------------|
| **Jan** | Budget expectations | Infra, PSU, Defense |
| **Feb** | Union Budget | Depends on allocations |
| **Mar** | FY-end tax selling, NAV window dressing | Broad market |
| **Apr** | New FY, fresh FII allocations | Large caps, banks |
| **May** | Summer demand, election season (if applicable) | Consumer durables, AC, beverages |
| **Jun** | Monsoon onset | Agri, FMCG rural, fertilizers |
| **Jul** | Q1 results season | Broad market |
| **Aug** | Monsoon mid-season, Independence Day | Agri, fertilizers |
| **Sep** | FII fund flow (post US labor day) | Large caps |
| **Oct** | Festive season (Navratri, Diwali) | Auto, FMCG, consumer durables, retail |
| **Nov** | Festive season continued, Q2 results | Auto sales data, FMCG |
| **Dec** | FII year-end, Santa rally, global cues | Broad market |

# Indian Market Sentiment & News Reaction Patterns

## How Indian Markets React to News Categories

### 1. RBI Monetary Policy

| Scenario | Immediate Reaction | Sectors | Typical Duration |
|----------|-------------------|---------|-----------------|
| **Rate cut (expected)** | Mild positive, already priced in | Banks +0.5-1%, Realty +1-2% | 1-2 days |
| **Rate cut (surprise)** | Strong positive rally | Banks +2-4%, Realty +3-5%, Auto +2-3% | 3-5 days |
| **Rate hold (expected cut)** | Negative, disappointment | Banks -1-2%, Realty -2-3% | 1-2 days |
| **Rate hike** | Negative broad market | Banks (mixed — NIM vs growth), Realty -3-5%, NBFCs -2-4% | 3-7 days |
| **Dovish commentary** | Positive, rate cut expectations build | Rate-sensitive sectors rally | 1-2 weeks |
| **Hawkish commentary** | Negative, rate hike fears | Rate-sensitive sectors decline | 1-2 weeks |

**Lag analysis:** Banks react immediately. Real estate lags by 1-2 days. Auto sector reacts with 2-3 day lag as loan rate transmission expectations build.

### 2. Union Budget

| Scenario | Typical Reaction | Key Patterns |
|----------|-----------------|--------------|
| **Infrastructure push** | Infra, cement, L&T rally 3-5% | Lasts 1-2 weeks then mean-reverts |
| **Defense allocation up** | HAL, BEL, BDL rally 5-10% | Sector re-rating over weeks |
| **Tax relief (personal)** | FMCG, consumer durables, auto up 2-3% | Consumer spending boost narrative |
| **Tax hike (capital gains)** | Market sell-off 2-5% | Recovers within 2-4 weeks |
| **Fiscal deficit overshoot** | Bond yields up, bank stocks dip | Medium-term negative for rate-sensitive |
| **Disinvestment target** | PSU stocks volatile | Initial pop on privatization names |

**Historical pattern:** Budget day often sees high volatility with 300-500 point Nifty swings. The initial reaction often reverses within 48 hours as market digests details.

### 3. Earnings Announcements

| Scenario | Price Reaction | Volume Pattern |
|----------|---------------|---------------|
| **Beat + raise guidance** | Gap up 3-10%, sustains | 3-5x normal volume |
| **Beat + inline guidance** | Gap up 1-3%, partial reversion | 2-3x normal volume |
| **Inline results** | Flat to -1% | Normal volume |
| **Miss + cut guidance** | Gap down 5-15% | 5-10x normal volume |
| **Miss + maintain guidance** | Down 2-5%, gradual recovery | 2-4x normal volume |

**India-specific nuances:**
- Large-cap results often "leak" before official announcement — watch for unusual pre-result price moves
- Results announced post-market (after 3:30 PM) impact next day's opening
- Results during F&O expiry week amplify options volatility
- "Analyst concalls" at 5-7 PM post-results often drive next-day gap direction

### 4. FII/DII Flow Events

| Scenario | Market Impact | Duration |
|----------|-------------|----------|
| **FII buying > ₹2000 Cr/day** | Nifty up 0.5-1% | Sustains if multi-day |
| **FII selling > ₹3000 Cr/day** | Nifty down 0.5-1.5% | Can persist for weeks |
| **FII selling + DII buying** | Market supported but choppy | "Tug of war" — range-bound |
| **Both FII + DII buying** | Strong rally, breadth expands | Can sustain for weeks |
| **Both FII + DII selling** | Rare, sharp correction | Usually brief (1-3 days) |

**Key insight:** FII selling in cash + buying in derivatives (long futures, short puts) is actually bullish. Look at combined cash + derivative position, not just cash data.

### 5. Crude Oil Price Shocks

| Scenario | Indian Market Impact | Key Stocks |
|----------|---------------------|-----------|
| **Crude > $90 (spike)** | Nifty -1-2%, OMCs hammered | BPCL, IOC, HPCL down 5-10%; ONGC up |
| **Crude > $100 (sustained)** | Broad market negative, INR weakens | Airlines, paints, plastics negative |
| **Crude < $70 (drop)** | Positive for India, INR strengthens | OMCs rally, Airlines rally |
| **Crude $70-85 (range)** | Neutral, no major impact | Goldilocks zone for India |

**Transmission lag:** Crude oil impact takes 2-4 weeks to flow into earnings. Fuel price revisions are the trigger — watch OMC marketing margins.

### 6. Global Events

| Event | Indian Market Correlation | Lag |
|-------|--------------------------|-----|
| **US Fed rate decision** | High for IT, Banks, FII flows | Same day (evening IST → next morning) |
| **US non-farm payrolls** | Medium — affects Fed expectations | Next trading day |
| **China PMI data** | Medium — affects metals, chemicals | 1-2 days |
| **US-China trade tensions** | Medium — China+1 narrative for India | Multi-week theme |
| **Geopolitical crisis** | High short-term, fades | 1-5 days depending on proximity |
| **Global risk-off (VIX spike)** | FII selling in India | 1-3 days lag, can persist weeks |

### 7. Corporate Governance / Fraud

| Scenario | Reaction | Recovery |
|----------|----------|----------|
| **Accounting fraud allegation** | -10-30% crash | Months to years (if true) |
| **Promoter arrest/ED raid** | -5-15% crash | Weeks if cleared, permanent if convicted |
| **Short seller report** | -10-25% crash, high volume | 2-4 weeks for dust to settle |
| **SEBI investigation** | -5-10% decline | Gradual over investigation period |
| **Auditor resignation** | -5-15% crash | Months — investors fear what's hidden |

**India-specific:** Adani-Hindenburg (Jan 2023) pattern — initial 25%+ crash, gradual recovery over months. Short seller reports on Indian companies have higher initial impact than global averages.

### 8. IPO Market Signals

| Signal | Market Implication |
|--------|-------------------|
| **High subscription (>10x retail)** | Bullish overall sentiment |
| **Multiple IPO withdrawals** | Bearish sentiment, risk-off |
| **Day-1 listing below issue price** | Cooling sentiment for new issues |
| **SME IPO frenzy** | Late-stage bullishness, potential froth |
| **PE/VC exits via OFS** | Mixed — smart money taking profits |

## Sentiment Indicators (India-Specific)

### India VIX Interpretation
| VIX Level | Market Regime | Trading Implication |
|-----------|-------------|-------------------|
| **< 12** | Extreme complacency | Breakout or breakdown incoming |
| **12-15** | Low volatility, trending | Ride the trend |
| **15-20** | Normal range | Standard strategies |
| **20-25** | Elevated fear | Hedging active, may be near bottom |
| **25-35** | High fear | Panic selling, contrarian opportunities |
| **> 35** | Crisis mode | Cash is king, wait for VIX to peak |

### Put-Call Ratio (Nifty)
| PCR Level | Signal | Interpretation |
|-----------|--------|---------------|
| **> 1.3** | Extreme put buying | Contrarian bullish (too many bears) |
| **1.0-1.3** | Moderately bearish | Cautious, hedge in place |
| **0.7-1.0** | Neutral to bullish | Normal market |
| **< 0.7** | Extreme call buying | Contrarian bearish (too many bulls) |

### FII Long/Short Ratio (Index Futures)
| Ratio | Signal |
|-------|--------|
| **> 70% long** | FII bullish, but watch for reversal at extremes |
| **50-70% long** | Moderately bullish |
| **30-50% long** | Bearish to neutral |
| **< 30% long** | Extremely bearish, contrarian buy zone |

## News Impact Decay Curve

Most news events follow a predictable decay pattern:

```
Impact
  │
  │ ████
  │ ████████
  │ ████████████
  │ ████████████████
  │ ████████████████████
  │ ████████████████████████
  │ ████████████████████████████
  │ ████████████████████████████████
  └──────────────────────────────────── Time
  Day1  Day2  Day3  Week1  Week2  Month1

  Typical pattern:
  - Day 1: 60-80% of total move
  - Day 2-3: 15-25% (continuation or reversion)
  - Week 1: Narrative builds or fades
  - Week 2+: Structural impact or full mean-reversion
```

**Exceptions to decay:**
- Earnings re-ratings → sustain for quarters
- Regulatory structural changes → sustain for months
- Fraud/governance → permanent derating
- M&A → sustains until deal completion/failure

## Contrarian Signals

When to go against the news narrative:

1. **Universal consensus** — When every headline says "market will crash/rally", the move is usually done
2. **Volume exhaustion** — If bad news arrives but selling volume is declining, bears are exhausted
3. **VIX spike + support hold** — Fear without price breakdown = accumulation
4. **FII selling + DII buying at support** — Smart domestic money stepping in
5. **Multiple downgrades at 52-week low** — Often marks the bottom
6. **Euphoric retail participation in SME IPOs** — Often marks the top

#!/usr/bin/env python3
"""
India Market News Fetcher
Fetches and categorizes Indian stock market news from RSS feeds.

Usage:
    # Daily briefing from all sources
    python3 news_fetcher.py

    # Stock-specific news
    python3 news_fetcher.py --stock RELIANCE

    # Sector news
    python3 news_fetcher.py --sector banking

    # Custom date range (days back)
    python3 news_fetcher.py --days 7

    # Output as JSON
    python3 news_fetcher.py --format json

    # Save to file
    python3 news_fetcher.py --output reports/daily_briefing.md
"""

import argparse
import json
import re
import sys
from datetime import datetime, timedelta
from typing import Optional
from dataclasses import dataclass, field, asdict

try:
    import feedparser
    HAS_FEEDPARSER = True
except ImportError:
    HAS_FEEDPARSER = False

try:
    import requests
    HAS_REQUESTS = True
except ImportError:
    HAS_REQUESTS = False

try:
    import yfinance as yf
    HAS_YFINANCE = True
except ImportError:
    HAS_YFINANCE = False


# ──────────────────────────────────────────────
# RSS Feed Sources
# ──────────────────────────────────────────────

RSS_FEEDS = {
    "moneycontrol_markets": {
        "url": "https://www.moneycontrol.com/rss/marketreports.xml",
        "source": "MoneyControl",
        "category": "Markets",
        "tier": 2,
    },
    "moneycontrol_news": {
        "url": "https://www.moneycontrol.com/rss/latestnews.xml",
        "source": "MoneyControl",
        "category": "General",
        "tier": 2,
    },
    "moneycontrol_business": {
        "url": "https://www.moneycontrol.com/rss/business.xml",
        "source": "MoneyControl",
        "category": "Business",
        "tier": 2,
    },
    "et_markets": {
        "url": "https://economictimes.indiatimes.com/markets/rssfeeds/1977021501.cms",
        "source": "Economic Times",
        "category": "Markets",
        "tier": 2,
    },
    "et_stocks": {
        "url": "https://economictimes.indiatimes.com/markets/stocks/rssfeeds/2146842.cms",
        "source": "Economic Times",
        "category": "Stocks",
        "tier": 2,
    },
    "livemint_markets": {
        "url": "https://www.livemint.com/rss/markets",
        "source": "LiveMint",
        "category": "Markets",
        "tier": 2,
    },
    "livemint_companies": {
        "url": "https://www.livemint.com/rss/companies",
        "source": "LiveMint",
        "category": "Companies",
        "tier": 2,
    },
    "business_standard": {
        "url": "https://www.business-standard.com/rss/markets-106.rss",
        "source": "Business Standard",
        "category": "Markets",
        "tier": 2,
    },
    "ndtv_business": {
        "url": "https://feeds.feedburner.com/ndtvprofit-latest",
        "source": "NDTV Profit",
        "category": "Business",
        "tier": 3,
    },
}

# ──────────────────────────────────────────────
# Event Classification Keywords
# ──────────────────────────────────────────────

EVENT_KEYWORDS = {
    "Earnings": [
        "quarterly results", "Q1", "Q2", "Q3", "Q4", "earnings", "profit",
        "revenue", "net income", "PAT", "EBITDA", "results declared",
        "topline", "bottomline", "YoY growth", "QoQ", "guidance",
    ],
    "Corporate Action": [
        "dividend", "bonus", "stock split", "buyback", "rights issue",
        "face value", "record date", "ex-date", "ex-dividend",
    ],
    "M&A": [
        "acquisition", "merger", "demerger", "takeover", "stake sale",
        "buyout", "amalgamation", "joint venture", "strategic investment",
    ],
    "Management": [
        "CEO", "MD", "chairman", "appointed", "resigned", "board",
        "managing director", "CFO", "key managerial",
    ],
    "Regulatory": [
        "SEBI", "RBI", "circular", "regulation", "compliance", "penalty",
        "norm", "guideline", "framework", "notification",
    ],
    "Institutional": [
        "FII", "FPI", "DII", "mutual fund", "bulk deal", "block deal",
        "institutional", "promoter", "insider trading", "SAST",
    ],
    "IPO": [
        "IPO", "initial public offering", "listing", "subscription",
        "allotment", "DRHP", "RHP", "anchor investor", "OFS",
    ],
    "Macro": [
        "GDP", "inflation", "CPI", "WPI", "IIP", "PMI", "trade deficit",
        "fiscal deficit", "current account", "unemployment",
    ],
    "Global": [
        "Fed", "US market", "Wall Street", "Nasdaq", "S&P 500", "Dow Jones",
        "crude oil", "dollar", "tariff", "global", "China", "recession",
    ],
    "Rating": [
        "upgrade", "downgrade", "target price", "outperform", "underperform",
        "buy rating", "sell rating", "hold rating", "analyst",
    ],
}

# ──────────────────────────────────────────────
# Sentiment Keywords
# ──────────────────────────────────────────────

BULLISH_KEYWORDS = [
    "rally", "surge", "soar", "gain", "jump", "rise", "bullish", "record high",
    "breakout", "upgrade", "outperform", "beat estimate", "strong results",
    "positive", "boom", "recovery", "expansion", "growth", "optimistic",
    "buying", "accumulate", "all-time high",
]

BEARISH_KEYWORDS = [
    "crash", "plunge", "sink", "fall", "drop", "decline", "bearish", "low",
    "breakdown", "downgrade", "underperform", "miss estimate", "weak results",
    "negative", "slump", "contraction", "slowdown", "pessimistic",
    "selling", "exit", "52-week low", "correction", "panic",
]

# ──────────────────────────────────────────────
# Sector Keywords
# ──────────────────────────────────────────────

SECTOR_KEYWORDS = {
    "Banking": ["bank", "HDFC", "ICICI", "SBI", "Kotak", "Axis", "NPA", "NIM", "credit growth", "deposit"],
    "IT": ["IT", "TCS", "Infosys", "Wipro", "HCL", "Tech Mahindra", "software", "digital", "AI", "cloud"],
    "Pharma": ["pharma", "drug", "FDA", "ANDA", "API", "hospital", "healthcare", "Sun Pharma", "Dr Reddy"],
    "Auto": ["auto", "Maruti", "Tata Motors", "Bajaj", "Hero", "EV", "electric vehicle", "sales data"],
    "FMCG": ["FMCG", "HUL", "ITC", "Nestle", "Britannia", "consumer", "rural demand"],
    "Realty": ["real estate", "realty", "DLF", "Godrej Properties", "housing", "RERA"],
    "Metal": ["metal", "steel", "Tata Steel", "JSW", "Hindalco", "aluminium", "iron ore", "copper"],
    "Energy": ["oil", "gas", "ONGC", "Reliance", "BPCL", "IOC", "crude", "refining", "energy"],
    "Infra": ["infra", "L&T", "construction", "highway", "railway", "smart city", "cement"],
    "Telecom": ["telecom", "Airtel", "Jio", "Vodafone", "5G", "spectrum", "ARPU", "subscriber"],
    "Power": ["power", "NTPC", "electricity", "renewable", "solar", "wind", "grid", "transmission"],
    "Defence": ["defence", "defense", "HAL", "BEL", "BDL", "missile", "military", "arms"],
}

# ──────────────────────────────────────────────
# NSE Stock Symbols (Common)
# ──────────────────────────────────────────────

STOCK_NAME_TO_SYMBOL = {
    "reliance": "RELIANCE", "tcs": "TCS", "infosys": "INFY", "infy": "INFY",
    "hdfc bank": "HDFCBANK", "hdfcbank": "HDFCBANK", "icici bank": "ICICIBANK",
    "icicibank": "ICICIBANK", "sbi": "SBIN", "state bank": "SBIN",
    "kotak": "KOTAKBANK", "axis bank": "AXISBANK", "wipro": "WIPRO",
    "hcl": "HCLTECH", "tech mahindra": "TECHM", "bharti airtel": "BHARTIARTL",
    "airtel": "BHARTIARTL", "itc": "ITC", "hindustan unilever": "HINDUNILVR",
    "hul": "HINDUNILVR", "larsen": "LT", "l&t": "LT", "bajaj finance": "BAJFINANCE",
    "maruti": "MARUTI", "tata motors": "TATAMOTORS", "sun pharma": "SUNPHARMA",
    "titan": "TITAN", "asian paints": "ASIANPAINT", "adani": "ADANIENT",
    "mahindra": "M&M", "m&m": "M&M", "power grid": "POWERGRID", "ntpc": "NTPC",
    "ultratech": "ULTRACEMCO", "nestle": "NESTLEIND", "bajaj auto": "BAJAJ-AUTO",
    "hero motocorp": "HEROMOTOCO", "dr reddy": "DRREDDY", "cipla": "CIPLA",
    "divis": "DIVISLAB", "grasim": "GRASIM", "britannia": "BRITANNIA",
    "godrej": "GODREJCP", "tata steel": "TATASTEEL", "jsw steel": "JSWSTEEL",
    "hindalco": "HINDALCO", "coal india": "COALINDIA", "ongc": "ONGC",
    "bpcl": "BPCL", "ioc": "IOC", "gail": "GAIL", "dlf": "DLF",
    "hal": "HAL", "bel": "BEL",
}


@dataclass
class NewsItem:
    """Represents a single news item."""
    title: str
    source: str
    published: str
    link: str
    category: str = "General"
    event_type: str = "Uncategorized"
    sentiment: str = "Neutral"
    impact_score: int = 3
    sectors: list = field(default_factory=list)
    stocks_mentioned: list = field(default_factory=list)
    summary: str = ""


def classify_event(title: str, summary: str = "") -> str:
    """Classify news into event type based on keywords."""
    text = (title + " " + summary).lower()
    scores = {}
    for event_type, keywords in EVENT_KEYWORDS.items():
        score = sum(1 for kw in keywords if kw.lower() in text)
        if score > 0:
            scores[event_type] = score
    if scores:
        return max(scores, key=scores.get)
    return "General"


def detect_sentiment(title: str, summary: str = "") -> str:
    """Detect sentiment from title and summary."""
    text = (title + " " + summary).lower()
    bull_score = sum(1 for kw in BULLISH_KEYWORDS if kw in text)
    bear_score = sum(1 for kw in BEARISH_KEYWORDS if kw in text)
    if bull_score > bear_score and bull_score >= 2:
        return "Bullish"
    elif bear_score > bull_score and bear_score >= 2:
        return "Bearish"
    elif bull_score > 0 and bear_score == 0:
        return "Bullish"
    elif bear_score > 0 and bull_score == 0:
        return "Bearish"
    return "Neutral"


def detect_sectors(title: str, summary: str = "") -> list:
    """Detect which sectors are mentioned."""
    text = (title + " " + summary).lower()
    sectors = []
    for sector, keywords in SECTOR_KEYWORDS.items():
        for kw in keywords:
            if kw.lower() in text:
                sectors.append(sector)
                break
    return sectors


def detect_stocks(title: str, summary: str = "") -> list:
    """Detect stock symbols mentioned in the text."""
    text = (title + " " + summary).lower()
    stocks = []
    for name, symbol in STOCK_NAME_TO_SYMBOL.items():
        if name in text and symbol not in stocks:
            stocks.append(symbol)
    return stocks


def score_impact(item: NewsItem) -> int:
    """Score the market impact of a news item (1-10)."""
    score = 3  # baseline

    # Event type scoring
    high_impact = ["M&A", "Regulatory", "Macro", "IPO"]
    medium_impact = ["Earnings", "Institutional", "Rating", "Global"]
    if item.event_type in high_impact:
        score += 2
    elif item.event_type in medium_impact:
        score += 1

    # Sentiment strength
    if item.sentiment in ("Bullish", "Bearish"):
        score += 1

    # Nifty 50 stocks get a boost
    nifty50_stocks = {
        "RELIANCE", "TCS", "HDFCBANK", "INFY", "ICICIBANK", "HINDUNILVR",
        "SBIN", "BHARTIARTL", "ITC", "KOTAKBANK", "LT", "AXISBANK",
        "BAJFINANCE", "MARUTI", "TATAMOTORS", "SUNPHARMA", "TITAN",
        "ASIANPAINT", "HCLTECH", "WIPRO", "NTPC", "POWERGRID",
    }
    if any(s in nifty50_stocks for s in item.stocks_mentioned):
        score += 1

    # Multiple sectors affected
    if len(item.sectors) >= 2:
        score += 1

    return min(10, max(1, score))


def fetch_rss_feeds(
    days_back: int = 1,
    stock_filter: Optional[str] = None,
    sector_filter: Optional[str] = None,
) -> list[NewsItem]:
    """Fetch news from RSS feeds."""
    if not HAS_FEEDPARSER:
        print("ERROR: feedparser not installed. Run: pip install feedparser")
        sys.exit(1)

    cutoff = datetime.now() - timedelta(days=days_back)
    all_items = []

    for feed_name, feed_info in RSS_FEEDS.items():
        try:
            feed = feedparser.parse(feed_info["url"])
            for entry in feed.entries[:20]:  # limit per feed
                # Parse published date
                published = ""
                if hasattr(entry, "published"):
                    published = entry.published
                elif hasattr(entry, "updated"):
                    published = entry.updated

                title = entry.get("title", "").strip()
                summary = entry.get("summary", "").strip()
                # Strip HTML tags from summary
                summary = re.sub(r"<[^>]+>", "", summary)[:300]
                link = entry.get("link", "")

                if not title:
                    continue

                item = NewsItem(
                    title=title,
                    source=feed_info["source"],
                    published=published,
                    link=link,
                    category=feed_info["category"],
                    summary=summary,
                )

                # Classify
                item.event_type = classify_event(title, summary)
                item.sentiment = detect_sentiment(title, summary)
                item.sectors = detect_sectors(title, summary)
                item.stocks_mentioned = detect_stocks(title, summary)
                item.impact_score = score_impact(item)

                # Apply filters
                if stock_filter:
                    stock_upper = stock_filter.upper()
                    stock_lower = stock_filter.lower()
                    if (
                        stock_upper not in item.stocks_mentioned
                        and stock_lower not in title.lower()
                        and stock_lower not in summary.lower()
                    ):
                        continue

                if sector_filter:
                    sector_lower = sector_filter.lower()
                    if not any(s.lower() == sector_lower for s in item.sectors):
                        # Also check title/summary for sector keyword
                        if sector_lower not in title.lower() and sector_lower not in summary.lower():
                            continue

                all_items.append(item)

        except Exception as e:
            print(f"Warning: Failed to fetch {feed_name}: {e}", file=sys.stderr)

    # Sort by impact score (descending), then by source tier
    all_items.sort(key=lambda x: (-x.impact_score, x.source))

    # Deduplicate by similar titles
    seen_titles = set()
    unique_items = []
    for item in all_items:
        # Simple dedup: normalize title
        normalized = re.sub(r"[^a-z0-9]", "", item.title.lower())[:50]
        if normalized not in seen_titles:
            seen_titles.add(normalized)
            unique_items.append(item)

    return unique_items


def get_stock_price(symbol: str) -> Optional[dict]:
    """Fetch current stock price using yfinance."""
    if not HAS_YFINANCE:
        return None
    try:
        ticker = yf.Ticker(f"{symbol}.NS")
        info = ticker.fast_info
        return {
            "symbol": symbol,
            "price": round(info.get("lastPrice", 0), 2),
            "change_pct": round(
                ((info.get("lastPrice", 0) - info.get("previousClose", 0))
                 / info.get("previousClose", 1)) * 100, 2
            ) if info.get("previousClose") else 0,
        }
    except Exception:
        return None


def format_sentiment_icon(sentiment: str) -> str:
    """Return emoji icon for sentiment."""
    icons = {
        "Bullish": "🟢",
        "Bearish": "🔴",
        "Neutral": "🟡",
    }
    return icons.get(sentiment, "⚪")


def format_markdown(items: list[NewsItem], stock_filter: Optional[str] = None,
                     sector_filter: Optional[str] = None) -> str:
    """Format news items as markdown."""
    now = datetime.now()
    lines = []

    if stock_filter:
        lines.append(f"# 📰 News Report — {stock_filter.upper()}")
    elif sector_filter:
        lines.append(f"# 📰 Sector News — {sector_filter.title()}")
    else:
        lines.append(f"# 📊 Daily Market News Briefing")

    lines.append(f"\n**Generated:** {now.strftime('%A, %d %B %Y %I:%M %p IST')}")
    lines.append(f"**Total Items:** {len(items)}")
    lines.append("")

    if not items:
        lines.append("No news items found for the given filters.")
        return "\n".join(lines)

    # High impact items (score >= 6)
    high_impact = [i for i in items if i.impact_score >= 6]
    if high_impact:
        lines.append("---")
        lines.append(f"\n## 🔥 High Impact News ({len(high_impact)} items)\n")
        for idx, item in enumerate(high_impact[:10], 1):
            icon = format_sentiment_icon(item.sentiment)
            lines.append(f"### {idx}. {item.title} — [{item.impact_score}/10] {icon}")
            lines.append(f"- **Source:** {item.source} | {item.published}")
            lines.append(f"- **Type:** {item.event_type} | **Sentiment:** {item.sentiment}")
            if item.sectors:
                lines.append(f"- **Sectors:** {', '.join(item.sectors)}")
            if item.stocks_mentioned:
                lines.append(f"- **Stocks:** {', '.join(item.stocks_mentioned)}")
            if item.summary:
                lines.append(f"- {item.summary[:200]}")
            lines.append(f"- [Read more]({item.link})")
            lines.append("")

    # Medium impact items (score 4-5)
    medium_impact = [i for i in items if 4 <= i.impact_score <= 5]
    if medium_impact:
        lines.append("---")
        lines.append(f"\n## 📰 Notable News ({len(medium_impact)} items)\n")
        for idx, item in enumerate(medium_impact[:15], 1):
            icon = format_sentiment_icon(item.sentiment)
            lines.append(f"**{idx}. {item.title}** [{item.impact_score}/10] {icon}")
            lines.append(f"   {item.source} | {item.event_type} | {', '.join(item.sectors) if item.sectors else 'General'}")
            if item.stocks_mentioned:
                lines.append(f"   Stocks: {', '.join(item.stocks_mentioned)}")
            lines.append("")

    # Low impact items (score 1-3)
    low_impact = [i for i in items if i.impact_score <= 3]
    if low_impact:
        lines.append("---")
        lines.append(f"\n## 📋 Other News ({len(low_impact)} items)\n")
        for item in low_impact[:10]:
            icon = format_sentiment_icon(item.sentiment)
            lines.append(f"- {icon} {item.title} — *{item.source}*")
        lines.append("")

    # Sector summary
    sector_counts = {}
    for item in items:
        for sector in item.sectors:
            sector_counts[sector] = sector_counts.get(sector, 0) + 1
    if sector_counts:
        lines.append("---")
        lines.append("\n## 📊 Sector Activity\n")
        lines.append("| Sector | News Count | Sentiment |")
        lines.append("|--------|-----------|-----------|")
        for sector, count in sorted(sector_counts.items(), key=lambda x: -x[1]):
            sector_items = [i for i in items if sector in i.sectors]
            bull = sum(1 for i in sector_items if i.sentiment == "Bullish")
            bear = sum(1 for i in sector_items if i.sentiment == "Bearish")
            if bull > bear:
                sent = "🟢 Bullish"
            elif bear > bull:
                sent = "🔴 Bearish"
            else:
                sent = "🟡 Mixed"
            lines.append(f"| {sector} | {count} | {sent} |")
        lines.append("")

    # Stocks mentioned
    stock_counts = {}
    for item in items:
        for stock in item.stocks_mentioned:
            stock_counts[stock] = stock_counts.get(stock, 0) + 1
    if stock_counts:
        lines.append("---")
        lines.append("\n## 🏢 Most Mentioned Stocks\n")
        lines.append("| Stock | Mentions | Price (Rs.) | Change |")
        lines.append("|-------|----------|-------------|--------|")
        for stock, count in sorted(stock_counts.items(), key=lambda x: -x[1])[:15]:
            price_info = get_stock_price(stock)
            if price_info:
                change_str = f"{price_info['change_pct']:+.2f}%"
                lines.append(f"| {stock} | {count} | {price_info['price']} | {change_str} |")
            else:
                lines.append(f"| {stock} | {count} | — | — |")
        lines.append("")

    # Event type distribution
    event_counts = {}
    for item in items:
        event_counts[item.event_type] = event_counts.get(item.event_type, 0) + 1
    if event_counts:
        lines.append("---")
        lines.append("\n## 📁 News by Category\n")
        lines.append("| Category | Count |")
        lines.append("|----------|-------|")
        for event, count in sorted(event_counts.items(), key=lambda x: -x[1]):
            lines.append(f"| {event} | {count} |")
        lines.append("")

    lines.append("---")
    lines.append("\n*⚠️ Disclaimer: For educational purposes only. Not investment advice.*")

    return "\n".join(lines)


def format_json(items: list[NewsItem]) -> str:
    """Format news items as JSON."""
    return json.dumps(
        {
            "generated_at": datetime.now().isoformat(),
            "total_items": len(items),
            "items": [asdict(item) for item in items],
        },
        indent=2,
        ensure_ascii=False,
    )


def main():
    parser = argparse.ArgumentParser(
        description="India Market News Fetcher — Fetch and categorize Indian stock market news"
    )
    parser.add_argument(
        "--stock", type=str, default=None,
        help="Filter news for a specific stock (e.g., RELIANCE, TCS)"
    )
    parser.add_argument(
        "--sector", type=str, default=None,
        help="Filter news for a sector (e.g., banking, IT, pharma, auto)"
    )
    parser.add_argument(
        "--days", type=int, default=1,
        help="Number of days to look back (default: 1)"
    )
    parser.add_argument(
        "--format", type=str, choices=["markdown", "json"], default="markdown",
        help="Output format (default: markdown)"
    )
    parser.add_argument(
        "--output", type=str, default=None,
        help="Save output to file (default: print to stdout)"
    )
    parser.add_argument(
        "--min-impact", type=int, default=1,
        help="Minimum impact score to include (1-10, default: 1)"
    )
    parser.add_argument(
        "--limit", type=int, default=50,
        help="Maximum number of items to return (default: 50)"
    )

    args = parser.parse_args()

    # Check dependencies
    if not HAS_FEEDPARSER:
        print("ERROR: 'feedparser' package is required.")
        print("Install it with: pip install feedparser")
        sys.exit(1)

    print(f"Fetching news (last {args.days} day(s))...", file=sys.stderr)
    if args.stock:
        print(f"Filtering for stock: {args.stock}", file=sys.stderr)
    if args.sector:
        print(f"Filtering for sector: {args.sector}", file=sys.stderr)

    # Fetch and process
    items = fetch_rss_feeds(
        days_back=args.days,
        stock_filter=args.stock,
        sector_filter=args.sector,
    )

    # Apply min impact filter
    items = [i for i in items if i.impact_score >= args.min_impact]

    # Apply limit
    items = items[:args.limit]

    print(f"Found {len(items)} news items.", file=sys.stderr)

    # Format output
    if args.format == "json":
        output = format_json(items)
    else:
        output = format_markdown(items, args.stock, args.sector)

    # Output
    if args.output:
        with open(args.output, "w", encoding="utf-8") as f:
            f.write(output)
        print(f"Report saved to: {args.output}", file=sys.stderr)
    else:
        print(output)


if __name__ == "__main__":
    main()