Real-Time Earnings,Real time news Sentiment Bot Development

this can be built as a real‑time earnings sentiment bot that ingests earnings calendars, pulls conference‑call transcripts, runs LLM analysis, and combines it with historical price‑reaction modeling to emit only strong positive or strong negative signals. Below is a concise, production‑oriented design covering data sources, pipeline, scoring logic, and deployment steps.

Data sources
Earnings calendars: A rolling feed of upcoming results to pre‑schedule monitoring and ingestion windows for each company. The BSE results calendar can be polled in parallel to catch late or revised disclosures and official timings. Media calendars provide cross‑checks and redundancy for any gaps or reschedules.

Transcripts and calls: Centralized repositories of India earnings transcripts and recorded earnings calls can be scraped or fetched to drive the LLM’s qualitative analysis layer. Corporate notices frequently publish official “Earnings Call Transcript” PDFs that can be parsed when third‑party transcripts lag. Dedicated “earnings conference call” hubs provide structured discovery when symbol‑level scraping is needed.

Prices and benchmarks: Daily EOD “bhavcopy” files power event‑study calculations and abnormal‑return baselining, with backup mirrors if file locations change. Exchange “All Reports” and index history provide reliable benchmarks and metadata for sector or index adjustments. Historical index series are used for market‑model or simple market‑adjusted returns in the event study.

Pipeline overview
Schedule and watchlist: A daily scheduler pulls the next 3–7 days of earnings from multiple calendars, building a symbol‑level watchlist with expected dates/times and fallback to exchange disclosures if calendars differ. On result day, a watcher polls exchange notices to capture the first official PDF/press release and timestamp the event for reaction windows.

Transcript ingestion: As transcripts post (or recordings become available), a fetcher downloads and OCR/parses PDFs or text to a normalized JSON with speaker tags, KPIs, guidance, and Q&A highlights for downstream prompts. If third‑party transcript hubs have it sooner, that version can be ingested first and later reconciled with the official filing.

LLM analysis: A prompt template extracts and scores beats/misses on revenue, EBITDA, margins, order book, guidance language, and tone polarity/uncertainty from the management commentary and Q&A, producing a normalized sentiment score in [−1, +1] and a structured rationale. When explicit guidance is given, the parser flags “guide up/down/inline” and attaches confidence from the exact statements for traceability.

Historical reaction model: The bot computes abnormal return windows such as
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to learn each company’s typical post‑earnings drift conditional on beat/miss/tone buckets, using EOD bhavcopies and index series. For robustness, a simple market‑adjusted model is the default, with optional sector adjustment when index composition matters.

Signal rules (only strong signals)
Strong Positive: Emit only if all are true: LLM tone score ≥ +0.6, explicit “guide up” or constructive outlook in transcript, and the issuer’s historical conditional median
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for similar setups is ≥ +1.5% with ≥ 65% positive hit‑rate over the past N events; attach succinct rationale with the specific metrics and quotes. If the official press release/transcript confirms a revenue/EBITDA beat with margin expansion, bias thresholds higher to reduce false positives.

Strong Negative: Emit only if all are true: LLM tone score ≤ −0.6, explicit “guide down”/cautionary language, and historical conditional median
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≤ −1.5% with ≥ 65% negative hit‑rate; attach rationale and excerpts for transparency. If the filing shows a miss with margin compression, increase confidence weighting and raise the negative threshold magnitude.

No Signal: If the evidence is mixed (e.g., tone positive but margins compress; or strong guidance without historical follow‑through), suppress output to avoid weak or ambiguous calls. This keeps outputs limited to only the two high‑conviction classes requested.

Event timing and reaction windows
Timestamp the “event” at the first official exchange disclosure of results or the earliest authoritative transcript/call time to anchor the T0 close/open windows consistently across names. Use two core windows: close‑to‑close
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for next‑day drift and
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for short‑term follow‑through analysis by cohort and issuer. Where needed, supplement with pre/post‑call price snippets but keep the signals anchored to EOD windows for execution simplicity.

Implementation sketch
Ingestion: Lightweight workers fetch calendars and notices on cron with exponential backoff and dedupe across sources to handle re‑filings and late updates. A PDF/text normalizer stores structured fields and tagged quotes for reproducibility and future re‑scoring as models improve.

Modeling: A rules‑plus‑LLM hybrid computes beat/miss/tone/guidance tags and then applies the issuer‑level calibrated event‑study thresholds to classify only strong positive/negative, logging the exact rules and metrics used per signal. Index and sector data are cached locally to stabilize the benchmark leg of abnormal returns.

Ops and delivery: Signals publish to a queue/webhook with symbol, side, confidence, key quotes, and back‑links to the source filings/transcripts for auditability; failed fetches retry with a fallback order from alternate calendars. A small dashboard shows the latest signals, their rationales, and a rolling precision/recall tracker per bucket to monitor drift.

Practical considerations
Source reliability: Prefer official exchange filings for event timestamps and primary facts; use media/transcript hubs for speed but reconcile against the filing for final classification. Maintain adapters for changing bhavcopy locations and formats to prevent pipeline breaks when URLs or schemas shift.

Backtesting and guardrails: Calibrate thresholds per issuer and sector using at least 8–12 prior events; require minimum sample sizes to avoid overfitting, and suppress signals for low‑history names until enough events accumulate. Keep a “no output on conflict” rule if sources disagree materially within the first hour to reduce spurious calls.

Minimal PoC next steps
Week‑ahead scope: Wire one earnings calendar source and one exchange notices feed; ingest 10–20 recent transcripts across 5–8 liquid large caps to seed the tone/guidance tagger.

Event study seed: Build baseline
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distributions per issuer using bhavcopy EOD and index series; validate that signal rules would have emitted on past clean “beat/guide‑up” and “miss/guide‑down” cases.

Dry‑run: Run live in “shadow mode” for one earnings week and compare precision/coverage against official transcript language and next‑day reaction to finalize strong‑signal thresholds.

This design yields a disciplined, auditable bot that triggers only high‑conviction earnings signals by fusing transcript‑level LLM analysis with issuer‑specific historical reaction patterns, anchored on exchange‑quality data.