IEOD Prompts

---

Step 1 — Parquet File Storage Rule

Requested:

Define the Parquet file storage rule:

• (Masid)_EQ — yearly file for spot/index scripts (underlying = 0)
• (Masid)_MONTHNAME — monthly file for F&O scripts (underlying > 0)

Based on these rules, update update_fo_file and update_index_file. Rename update_index_file → update_EQ_file.

IEOD data is no longer stored in Postgres — all intraday data lives in Parquet files only.

Parquet Store Layout:

nseieoddata/
  N/NIFTY/
    2024/
      1_EQ              ← NIFTY spot, all of 2024  (masid=1, underlying=0)
      1_JANUARY         ← NIFTY near-month fut, January expiry
      1_FEBRUARY
      ...
  B/BANKNIFTY/
    2024/
      2_EQ              ← BANKNIFTY spot  (masid=2, underlying=0)
      2_JANUARY
      ...

Rule summary:

Script type	underlying	File pattern	Frequency
Spot / Index	0	{masid}_EQ	One file per year
Futures / Options	> 0	{masid}_{MONTH_NAME}	One file per expiry month

Implementation status:

Item	Status
Parquet store layout (_EQ yearly + _MONTHNAME monthly)	✓ Implemented in gdfl/ pipeline
update_fo_file — writes F&O data to monthly Parquet	✓ Implemented
update_index_file → renamed to update_EQ_file	⚠ Pending — update_index_file still exists in nseIEOD.py; only prints ticker list, insert logic not yet written
IEOD data no longer written to Postgres	✓ scr_nsefo / scr_nseeq staging tables cleared after pack

Files involved:

• backend/nseieod/nseIEOD.py — IEod.update_fo_file(), IEod.update_index_file() (to be renamed)
• backend/nseieod/gdfl/gdfl_zip_extractor.py — GDFL CSV → Parquet writer
• backend/nseieod/gdfl/step1_convert_yearly.py — packs monthly files → yearly _EQ

---

Step 2 — nseieod CLAUDE.md Rules

Requested:

Include the following rules in CLAUDE.md in backend/nseieod/:

1. Always use class IEod for IEOD data ingestion and updates
2. Always use class Con_Price_Getter for pulling data from Parquet files and Postgres DB
3. Any changes made in backend/nseieod/nseieod.py must be documented in docs/developer/packages/nseieod/nseieod-main.md

Implementation:

Created backend/nseieod/CLAUDE.md with the following rules:

Rule 1 — IEod is the only entry point for ingestion
Rule 2 — Con_Price_Getter is the only entry point for reading prices
Rule 3 — nseIEOD.py changes must be reflected in nseieod-main.md

Class responsibilities:

Class	File	Purpose
IEod	nseIEOD.py	Ingests GDFL CSV → Parquet; packs staging DB → Parquet
Con_Price_Getter	nseIEOD.py	Reads price series from Parquet (and Postgres fallback)

Never call Parquet files directly from outside nseieod/ — always go through Con_Price_Getter.
Never write intraday data outside of IEod methods.

Files involved:

• backend/nseieod/CLAUDE.md — rules file (created)
• backend/nseieod/nseIEOD.py — contains both classes
• docs/developer/packages/nseieod/nseieod-main.md — must be updated when nseIEOD.py changes

Step 3 — Merge get_ieod_data into Con_Price_Getter

Requested:

Refer to get_ieod_data from ieod_exp.py and insert the same logic into Con_Price_Getter in nseIEOD.py. After review, ieod_exp.py will be deleted.

What get_ieod_data has that Con_Price_Getter currently lacks:

Feature	get_ieod_data (ieod_exp.py)	Con_Price_Getter.__get_contract_month
_EQ year cache	✓ Reads yearly file once, reuses for each month	✗ Re-reads the full _EQ file on every month iteration
Legacy monthly spot fallback	✓ Falls back to {root}_{MONTH} if _EQ missing	✗ Returns empty if _EQ not found — no fallback
Month name casing	✓ UPPER() → 1_JANUARY (matches file names)	✗ Title case January → may miss files
Takes masid directly	✓ No script-name string lookup needed	— works differently (resolves via script name + opttype)
Flexible date parsing	✓ Handles YYYY-MM-DD, DD-MM-YYYY, DD/MM/YYYY	— uses pd.to_datetime directly

Key improvement — eq_cache:

get_ieod_data caches the yearly _EQ file per year so it is only read from disk once, even when iterating over 12 months in a year query:

eq_cache = {}   # year → DataFrame

for month_start in months:
    if year not in eq_cache:
        eq_cache[year] = pd.read_parquet(eq_path)   # read once
    chunk = eq_cache[year]
    chunk = chunk[chunk['sdate'].dt.month == month_start.month]

Con_Price_Getter.__get_contract_month currently calls pd.read_parquet inside the loop — reads the same file up to 12 times for a full year query.

Key improvement — legacy fallback:

# get_ieod_data — tries new format first, falls back gracefully
eq_path = os.path.join(year_dir, f"{root_id}_EQ")
if os.path.exists(eq_path):
    # use yearly file
    ...
    continue   # skip monthly path entirely for spot

# Legacy fallback — pre-2016 monthly spot files
legacy_path = os.path.join(year_dir, f"{root_id}_{month_name}")

Implementation plan:

1. Add a get_prices_by_masid(masid, from_date, to_date) method to Con_Price_Getter that contains the get_ieod_data logic (cache + legacy fallback + UPPER month names)
2. Refactor __get_contract_month to call this shared logic internally
3. Verify output matches get_ieod_data on a sample masid/date range
4. Delete backend/nseieod/ieod_exp.py
5. Update docs/developer/packages/nseieod/nseieod-main.md

Status: ✓ Implemented — ieod_exp.py deleted, logic merged into Con_Price_Getter.

fine_tune Con_Price_Getter

Requested

1. Remove datapath parameter from Con_Price_Getter — get the path from env via Config.IEOD_DATA_PATH.
2. Create backend/scratch/getprices.py — fetch NIFTY IEOD data (1-1-2009 → 31-12-2011) using Con_Price_Getter and save as nifty_2009_2011.csv in Config.SCRATCH_CSV_PATH.

Changes made

backend/nseieod/nseIEOD.py

• Con_Price_Getter.__init__ signature changed:

  # Before
  def __init__(self, script, datapath, fromdate="", ...)
  
  # After
  def __init__(self, script, fromdate="", ...)

• self.datapath is now set internally from Config.IEOD_DATA_PATH — callers no longer pass a path.
• from app.config import Config added at module top.

backend/scratch/getprices.py (new file)

cp = Con_Price_Getter(
    script   = "NIFTY",
    fromdate = "1-1-2009",
    todate   = "31-12-2011",
    opttype  = "SPOT",
)
df = cp.getprices()
df.to_csv(os.path.join(Config.SCRATCH_CSV_PATH, "nifty_2009_2011.csv"), index=False)

Run from backend/:

python -m scratch.getprices

Implementation status

Item	Status
datapath param removed from Con_Price_Getter	✓ Done
Config.IEOD_DATA_PATH used internally	✓ Done
backend/scratch/getprices.py created	✓ Done
Output saved to Config.SCRATCH_CSV_PATH/nifty_2009_2011.csv	✓ Done

GDFL Extraction

Requested

Extract daily CSV files from GDFL ZIP archives in CSV_PATH into yearly sub-folders, then check for missing trading days and write a report.

• Source: ZIP files in Config.CSV_PATH
• Output: CSV_PATH/{year}/{filename}.csv
• Skip all BACKADJUSTED files
• Three file types: Index (GFDLCM_INDICES_), Equity (GFDLCM_EQ), FO (GFDLNFO_CONTRACT_, GFDLNFO_NIFTY_BNK)
• After extraction, compare extracted dates against get_nse_working_days() from liquid.py
• Write missing dates to CSV_PATH/missing.txt

Implementation

Created backend/nseieod/gdfl/extract.py (Part 1).

Run from backend/:

python -m nseieod.gdfl.extract                  # extract 2016-today
python -m nseieod.gdfl.extract --dry-run         # preview, no writes
python -m nseieod.gdfl.extract --from 2020       # start from 2020
python -m nseieod.gdfl.extract --from 2016 --to 2022

Output structure:

CSV_PATH/
  2016/
    GFDLCM_INDICES_01012016.csv
    GFDLCM_EQ_01012016.csv
    GFDLNFO_NIFTY_BNK_OPT_01012016.csv
    ...
  2017/
    ...
  missing.txt   <-- missing dates report per file type

missing.txt format:

GDFL Missing Date Report
Generated : 2026-06-03 11:00:00
Range     : 2016-01-01  to  2026-06-03

Index files  -- 3 missing date(s)
  2016-01-15
  2016-03-22
  ...

Equity files -- 0 missing date(s)
  (none -- all working days present)

FO files     -- 5 missing date(s)
  ...

Implementation Status

Item	Status
extract.py created in backend/nseieod/gdfl/	Done
ZIP scan from Config.CSV_PATH	Done
BACKADJUSTED files skipped	Done
Index / Equity / FO classification	Done
Year subfolder extraction	Done
Missing date check via get_nse_working_days()	Done
missing.txt written to CSV_PATH	Done
--dry-run, --from, --to CLI flags	Done

GDFL Fix1.0

Problem: Top-level ZIPs contain inner ZIPs (e.g. monthly bundles). The original code only scanned for .csv at the top level — inner ZIPs were invisible, so nothing was extracted.

Fix: extract.py rewritten with recursive ZIP handling.

• Inner .zip entries inside any ZIP are opened in memory (io.BytesIO) and recursively scanned
• Handles arbitrary nesting depth
• Output log now shows Inner ZIPs opened: N count

Status: Fixed in backend/nseieod/gdfl/extract.py

GDFL Fix1.1

Start Date 1 JUN 2016 End Date 31-10-2024

Do it for Every Year

1.) Get NSE Working from get_nse_working_days(in liquid.py of nseieod) 2.) Check Corresponding GFDL_file Exists in corresponding directory By Knowing the following information

i) from 2016-Mid 2020 Until file prefix has(GFDLNFO_NIFTY_BNK_OPT_) only Nifty and bank nifty optionsdata there(nfo) no EQ!! ii) from Mid 2020 (GFDLNFO_CONTRACT_) IT ALSO has NFO (includes all stocks F&O) Till Mid 2022 iii) from mid 2022 there should 3 files

a.GFDLCM_INDICES_ (Index data) EQ Segment b.) GFDLCM_STOCK_(Stock Data) Stocks EQ c.) GFDLNFO_CONTRACT_(all F&O) contract Pls report if any missing files

to do above task Pls update check-files.py in gdfl folder(over write existing code)

at Last tell me how to run

Enhance Ieod.py 1.1

1.) Pls Create a Helper file under nseieod folder that stores common function

and Insert following function

1.) MarketOpeningTime(Date)

if Date before 2010-01-04 it is 9:55AM other wise it is 9:15 AM

2.) MarketClosingTime(Date) So far 15:30

So for nseIEOD.py Con_Price_Getter Accepts only (fromdate and todate) Whole date

Pls modify this class as it accepts both and date and time

For eg FromDate="2011-01-01 09:30:00: todate:"2011-02-01"

is equal to FromDate="2011-01-01 09:30:00: todate=todate+ " "+MarketClosingTime(todate)

Fix markettime 1.2

on Special Day(which got from Postgres Table ) market time based on Start_time and end_time as per db Pls change MarketOpeningTime(Date) and MarketClosingTime(Date) functions as per that

Filling_Utclass 1.0

Pls Refer ut_Classes.py it has 3 classes which serves refilling missing datas

1.) Resu_Holidays Don't touch this class we will check this latter 2.) FillingMissingTimes

During this Always use helpers.py get_nse_working_days,MarketOpeningTime,MarketClosingTime to get expected bars

it fills 3 Types fill a.) One fill bar fill if any bar missing it fill with pre bar data Here I I need one more improvement if first bar missing (MarketOpeningTime) then fill with next bar

these type filling should be marked as -1 in "filled' columns make change (currently it stores just 1)

b.) For 2-5 Consecutive Minutes

use Linear interpolation on Close

100 100.2 100.4 100.6 100.8 101

Then build synthetic OHLC

Mark these type of with filled=-2

c.) For 5-20 Consecutive Minutes

Then fill with Prev day Pattern (Pls refer __get_prev_day_pattern)

if consecutives bars less than 20 marks this type as filled=-3

if missing bar >20 then fill with filled=-4

3.) Next Restore_Missingdates class

This Class has retrieve Exact eod data (OHLC) from post gres db

then we may get 3 Patteren

i.) Open to (High or low) ii.)(High or low) to (High or low) //Pattern bettewn high and low iii.)(High or low) to close

after identifying these 3 patterns fill with Prev day Pattern with filled=-5

if more than one consecutive days missing fill with filled=-6

Note all these filling must fill with Volume=0

if filling has less than -3 These data clusters must be replaced with data with some other data vendor

Based on above Information Pls change ut_Classes.py

Rawfolder

Before processing each file Copy the existing file in another sub folder raw under IEOD_DATA_PATH

Pls copy along with it's folder structure after copying this we may know which file processed

Checkpriceswithtime

1.) In Recent Changes I dropped Sdetails.py and Check_qc.py So Pls Remove it's documentation if docs folder 2.) Nseieod/getprices now accepts only whole days Pls the that why can also accept time value also So that I can filter prices between 10:00 AM to 12:00 AM(Pls I have changed Con_price_Getter as per that)

LiquidScripts

Create a Table in duck_db to maintain Liquid Scripts

Currently only below these

Liquid scripts
--------------
  masid=1  NIFTY spot           -> N/NIFTY/{year}/1_EQ
  masid=2  BANKNIFTY spot       -> B/BANKNIFTY/{year}/2_EQ

Store this In to LiquidScripts table in duck_db

along with spot_start_date,cfut_start_date,fut_start_date,checked_till,last_checked_date

Cfut Means continuous future

StartDate Check

Before checking a data it should know whether it is SPOT or CFuture Underline>0 and monthindex>0 or Future Underline>0 and monthindex=0 based on these Starting date should Retrieved from liquid_scripts from duck_db

purpose of Retrived Start is Check should not performed before StartDate if Start date defined in liquid_scripts

Pls Implement this in check in fix_data

Check_options_Index

I want to audit The Scripts Stored in Parquet files

Parameter

FromDate :2016-06-01

Todate:2020-05-31" Scripts['NIFTY','BANKNIFTY']

As You Know from June 2016 I am Storing Only Banknifty and nifty OPtions(both weekly and monthly) Till May 2020

In this Period I want check Liquidity of ATM,OTM1,OTM2,OTM3,OMT4,ITM1,ITM2,ITM3,IMT4 of Options Contracts

ATM should derived from Prev.day Close

We Can Get EOD Close prices from scr_nseeq_eod for Scripts(masid can be derived from scr_master)

Their Derived contracts can get from below query

SELECT DISTINCT sm.masid, sm.mname, sm.typ, sm.edate, sm.strike, sm.opttyp, sm.monthindex FROM scr_master sm JOIN scr_master nifty ON nifty.mname = 'NIFTY' JOIN scr_nsefo_eod eod ON eod.masid = sm.masid WHERE (sm.masid = nifty.masid OR sm.underly = nifty.masid) AND eod.sdate >= DATE '2016-06-01' AND eod.sdate < DATE '2017-01-01' and sm.typ>3 ORDER BY sm.edate, sm.strike, sm.opttyp, sm.monthindex, sm.mname;

Using similar Query we can get all contracts

from this Information you have check liquity or missing data for ATM,OTM1,OTM2,OTM3,OMT4,ITM1,ITM2,ITM3,IMT4 Data along with EQ,CFut (Like fix_data.py Check)

You can write this in audit.py(later we will merge all on single file)

if need we can fill

Implementation

Created backend/nseieod/audit.py with public audit(from_date, to_date, scripts).

Run from backend/:

python -m nseieod.audit --from 2016-06-01 --to 2020-05-31
python -m nseieod.audit --from 2016-06-01 --to 2016-12-31 --scripts NIFTY

How it works (per root, per trading day):

1. ATM from prev-day close — spot EOD close is read from scr_nseeq_eod (masid = spot). The ATM strike is the nearest available strike to the previous working day's close.
2. Ladder — ATM, OTM1-4, ITM1-4 taken from the actual strike grid of each live expiry (both CE and PE → 18 contracts/expiry). Moneyness is assigned per option type (CE: above ATM = OTM; PE: above ATM = ITM).
3. Live expiries — every expiry with edate >= trade_date within a 100-day horizon. Weekly vs monthly is derived from edate (the last expiry of a calendar month = monthly M), not typ — in scr_master all of these options are stored as typ=5.
4. EOD-gated — a ladder contract is audited only if it actually traded that day, i.e. it has a row in scr_nsefo_eod. Deep OTM/ITM strikes that never traded are ignored (not reported as missing data), per the project leader's instruction.
5. Coverage — each audited contract's intraday Parquet bars ({IEOD_DATA_PATH}/{L}/{NAME}/{year}/{root}_{TRADE_MONTH}) are counted vs the full session. Options trade intermittently, so per-minute gap clustering is not used; coverage % is the liquidity signal.

Report (two views):

• [A] NO intraday bars — contracts that traded per EOD yet have zero 1-minute bars in Parquet. These are the genuine missing-data cases.
• [B] Liquidity profile — coverage % (bars / full session) by moneyness (n_days, mean, median, min, %<50%, %zero), so ATM-vs-OTM/ITM liquidity is visible at a glance.

The EQ (spot) and CFut (near-month future) coverage is checked by delegating to fix_data.check for the same roots (non-fatal if the DuckDB catalog is locked). The full report is tee'd to Config.CSV_PATH/audit_{from}_{to}_{ts}.txt.

Status: Implemented in backend/nseieod/audit.py. Verified on NIFTY + BANKNIFTY for 2016-06 → 2016-07 (the only window currently converted to Parquet): 0 missing-intraday contract-days; ATM/near-money median coverage ~80-90%, tailing off for deep ITM as expected. Filling is not implemented yet ("if need we can fill").

Note: 2017-2020 option Parquet is not yet ingested — run IEod.update_folder() for those years first (writes to the root store), then re-run the audit.

Item	Status
audit.py created	Done
ATM from prev-day close (scr_nseeq_eod)	Done
ATM/OTM1-4/ITM1-4 ladder (CE+PE) from real strike grid	Done
All live expiries (weekly + monthly from edate)	Done
EOD-gated on scr_nsefo_eod (ignore untraded deep strikes)	Done
Coverage report + liquidity profile	Done
EQ/CFut check (delegates to fix_data.check)	Done
Fill missing/low-liquidity option bars	Pending

Audit format

For Options I require below format report

Date	Script	EodPrice	Opt	Strike	Type	missingBars	Time	Filling
2016-07-08	BANKNIFTY	17321	CE	17300	ATM	2	9:25:00	-2; 2-5 bar linear interpolation
2016-07-08	BANKNIFTY	17321	CE	17200	ITM1	2	9:25:00	-2; 2-5 bar linear interpolation
2016-07-08	BANKNIFTY	17321	PE	17200	OTM1	2	9:25:00	-2; 2-5 bar linear interpolation
for Bank nifty Nifty Spot and CFut I require Following format if it is missing more that 3 working days(whole Day)

Script	Last Spot IEOD Date	Last Spot Cfut Date

Implementation

backend/nseieod/audit.py now prints two additional report sections (run via python -m nseieod.audit --from ... --to ... --scripts ...):

[Option Gaps] -- one row per consecutive missing-1-minute cluster for every audited (EOD-traded) ladder contract, in the requested format:

| Date | Script | EodPrice | Opt | Strike | Type | missingBars | Time | Filling |

• EodPrice is the previous day's spot close used to derive the ATM strike (same value _ladder() was given).
• Type is the moneyness label (ATM, OTM1-4, ITM1-4).
• missingBars / Time come from fix_data._find_clusters() -- the cluster's bar count and start time (H:MM:SS, no leading zero, matching the example).
• Filling is fix_data._fill_note() (the filled-flag convention from nseieod/CLAUDE.md rule 7: -1 single-bar carry, -2 2-5 bar interpolation, -3 6-20 bar previous-day pattern, -4 >20 bar -- replace with another vendor), reformatted with a space after the ;.

[EQ/CFut Staleness] -- one row per script, only when the spot (IEOD) or near-month continuous future (CFut) Parquet data is missing more than 3 whole NSE working days as of to_date:

| Script | Last Spot IEOD Date | Last Spot Cfut Date |

• _last_data_date() walks NSE working days backwards from to_date (via fix_data.check_day) until it finds a day with bar_count > 0.
• A script is omitted entirely if both spot and CFut are within 3 working days of to_date (i.e. "up to date").
• None means no data was found for that segment anywhere in [from_date, to_date] -- expected for CFut during the 2016-06 -> 2020-05 options-only GDFL era.

Status: Implemented and verified for BANKNIFTY 2016-06-01 -> 2016-06-10 -- [Option Gaps] printed per-cluster rows with Filling codes -1 through -4 across the ladder, and [EQ/CFut Staleness] correctly flagged BANKNIFTY's near-month CFut as None (no CFut Parquet data in this options-only period).

Item	Status
[Option Gaps] table (Date/Script/EodPrice/Opt/Strike/Type/missingBars/Time/Filling)	Done
[EQ/CFut Staleness] table (Script/Last Spot IEOD Date/Last Spot Cfut Date), >3 working-day gate	Done

GDFL Detailed Quality Check

Pls Modify nseieod audit.py so that it will generate report (Quality check Report) Pls refer attached CSV

Since Report is so wide Pls generate as reading format

To generate Report You have to following

1.) As You know ATM,OTM1,ITM4 calculated base on Pre.date EOD Close price 2.) Get Current Date OHLC Data from Post gres(Spot) 3.) Get Options Scripts of Both CE&PE (ATM,OTM 1to 4 ,ITM 1to 4) compress that in daily time frame 4.) Check Open,High,Low Not Close(since NSE calculates EOD Close as Average of last 30 MIn it is not LTP where as one minute data would give ltp) 5.) After getting both datasets now Compare two for the Day if it matches Place (for 17500CE-OK) or 17500CE-High Not matching) Like that

if any data (strike Missing ) it should be in both Data sets if missing from GDFL(One minute) Pls report... if any data fould in GDFL but not in EOD That also should be reported

Pls Not it should checked from only ranges given in Parameer also note in 2016 Only Bank nifty has weekly options Nifty has only monthly options

Pls check with out asking much from me

Implementation

backend/nseieod/audit.py now generates a GDFL Detailed Quality Check report. It is printed first by audit() (every run) and can be run on its own with --quality-only.

Run from backend/:

python -m nseieod.audit --from 2016-06-01 --to 2016-06-30 --quality-only
python -m nseieod.audit --from 2016-06-01 --to 2016-06-30 --scripts BANKNIFTY --quality-only
python -m nseieod.audit --from 2016-06-01 --to 2020-05-31            # quality + coverage + staleness

How it works (per root, per trading day):

1. ATM from prev-day close — the ATM strike and the ATM, OTM1-4, ITM1-4 ladder (CE+PE) are derived from the previous working day's spot EOD close (scr_nseeq_eod), reusing the existing _ladder / _moneyness helpers.
2. Current-day spot OHLC — read from scr_nseeq_eod and shown in each day's header (Spot O/H/L/C ... prevClose ...).
3. Two daily OHL snapshots per option contract:
   • EOD — sopen/shigh/slow from scr_nsefo_eod (_eod_option_ohlc).
   • GDFL — the 1-minute Parquet bars compressed to a daily bar (Open = first IST bar's sopen, High = max(shigh), Low = min(slow)) via _month_option_ohlc.
4. Compare Open/High/Low only — Close is intentionally skipped (NSE EOD close = average of the last 30 min, not the LTP; the 1-minute series gives an LTP-style close). Exact match required: OHLC_TOL = 0.0, with both sides rounded to 2 decimals (paise) so the equality is immune to float noise (NSE option prices are multiples of 0.05, so rounding is lossless).
5. Status per contract — {strike}{CE/PE} {moneyness} followed by:
   • OK — present in both, O/H/L all match.
   • High/Low/Open Not matching (...) — present in both, with the differing field(s) and both values.
   • MISSING from GDFL (traded per EOD) — EOD row exists, no 1-minute bars.
   • EXTRA in GDFL (not in EOD) — 1-minute bars exist, no EOD row.
   • Strikes absent from both datasets (deep OTM/ITM never traded) are ignored.

Two outputs:

• Console — reading (tall) layout: grouped Date → Script → Expiry, one line per contract (all live expiries), with a one-line OK / Mismatch / MissingFromGDFL / ExtraInGDFL summary. Tee'd to Config.CSV_PATH/audit_{from}_{to}_{ts}.txt.
• Wide CSV — BNFStrikes.csv layout (the project leader's reference file): one row per trading day for the nearest (front) expiry only, written to Config.CSV_PATH/{SCRIPT}_strikes_{from}_{to}_{ts}.csv. Columns Date, Prev.eod, Expiry, ATM(CE), ATM(PE), OTM1(CE), OTM1(PE), ITM1..., OTM4(CE), OTM4(PE), ITM4(CE), ITM4(PE); each cell is {strike}{CE|PE}-{token} where token ∈ OK (O/H/L all match), NotOK (any of O/H/L differs), MISS (EOD but no GDFL), EXTRA (GDFL but no EOD). The file is prefixed with a Script,{name} header line to mirror the reference exactly.

Verified the wide CSV reproduces BNFStrikes.csv cell-for-cell (same columns, 1-Jun-16 date format, front-expiry selection, and strike-per-slot mapping). The reference's NotOK cells are illustrative placeholders — in the actual Parquet store the GDFL daily O/H/L matches scr_nsefo_eod (e.g. 2016-06-01 17700CE: both O/H/L = 72.7/72.7/5.7 over a full 376-bar session → OK).

The 2016 weekly/monthly rule needs no special-casing: NIFTY simply has no weekly expiries in scr_master for that period, so its ladder only ever resolves monthly ([M]) expiries, while BANKNIFTY resolves both [W] and [M].

Status: Implemented in backend/nseieod/audit.py. Verified on the ingested window — BANKNIFTY 2016-06-01 → 2016-06-07: 237 contract-days, all OK (GDFL daily O/H/L matches scr_nsefo_eod exactly); NIFTY 2016-06 resolves monthly expiries only; BANKNIFTY 2018-03 (EOD present, no Parquet yet) correctly reports every ladder contract as MISSING from GDFL.

Item	Status
quality_check() + --quality-only CLI flag	Done
ATM/OTM1-4/ITM1-4 (CE+PE) ladder from prev-day close	Done
Current-day spot OHLC from scr_nseeq_eod in header	Done
GDFL 1-min → daily O/H/L compression	Done
EOD O/H/L from scr_nsefo_eod	Done
Compare Open/High/Low only (Close skipped by design)	Done
MISSING-from-GDFL / EXTRA-in-GDFL reporting	Done
Tall "reading" layout (Date → Script → Expiry) + summary	Done
Wide CSV in BNFStrikes.csv layout (front expiry, per script)	Done
2016 NIFTY monthly-only / BANKNIFTY weekly+monthly	Done (intrinsic)

Versioninfo

good write a wrapper function that pack(a year or from date and todate ) in nseieod.py

That imports from Extracted .csv file in CSV Path

By Knowing below knowledge

Knowledge

Version=1 from JUN-2016 to 15-may-2020 fo_prefix=GFDLNFO_NIFTY_BNK_OPT_ index_prefix = "" q_prefix = ""

Version =2 from 16 May 2020 to Jan 03 2021

fo_prefix="GFDLNFO_CONTRACT_" index_prefix = "" eq_prefix = ""

Version =3 from Jan 04 2021 to current date fo_prefix="GFDLNFO_CONTRACT_" index_prefix = "GFDLCM_INDICES_" eq_prefix = "GFDLCM_STOCK_"

Pls Note Before Jan 04 2021 Index Spot Files and Cfut One Minute Data GDFL did not provided we have import from other source(only Index and future Data)

from Jan 04 2021 GDFL Provide all data

Important from this data we Have calculate Continous Future data from GDFL Data

Pls add This knowledge in claude.md also

Implementation

Added GDFL_VERSIONS (the table above, as data) and a module-level wrapper pack_gdfl_range(year=None, fromdate=None, todate=None, datapath=None) to backend/nseieod/nseIEOD.py, right after the IEod class.

What it does:

1. Resolves the requested range from either year= or fromdate=/todate=.
2. Splits it at the GDFL version boundaries (2020-05-15/16, 2021-01-03/04).
3. Within each version segment, splits further by calendar year (extracted CSVs live under {Config.CSV_PATH}/{year}/, per gdfl/extract.py).
4. For each (version, year) chunk, constructs a fresh IEod with the correct fo_prefix/version for that period and calls the existing IEod.pack() (CSV → Parquet directly, no scr_nsefo/scr_nseeq staging).
5. A version-3 segment (which also has Index/EQ GDFL files) prints a note instead of silently skipping them, since update_index_file() / update_eq_file() are still stubs — only FO import is wired end-to-end.

from nseieod.nseIEOD import pack_gdfl_range

pack_gdfl_range(year=2017)
pack_gdfl_range(fromdate=date(2020, 1, 1), todate=date(2021, 6, 30))  # spans v2 -> v3

Verified: pack_gdfl_range(fromdate=2017-01-02, todate=2017-01-03) correctly auto-selected version=1, fo_prefix=GFDLNFO_NIFTY_BNK_OPT_; a range spanning 2020-05-10 → 2020-05-20 correctly split into 2 segments at the v1/v2 boundary (2020-05-15/16), each with the right prefix.

The version/prefix knowledge table was added to nseieod/CLAUDE.md as rule 9, and rule 8's GDFL coverage-breakup table was corrected to the precise dates given here (it previously said the EQ-onward period started Mar 2022; the correct date is 4 Jan 2021).

Item	Status
GDFL_VERSIONS table (version/fo_prefix/index_prefix/eq_prefix per period)	Done
pack_gdfl_range() wrapper — auto version/prefix resolution	Done
Splits range at version boundaries + calendar-year boundaries	Done
Knowledge added to nseieod/CLAUDE.md (rule 9)	Done
Rule 8 coverage table corrected (Mar 2022 → 4 Jan 2021)	Done
Index/EQ import for version 3	Pending (update_index_file/update_eq_file still stubs)

VersionUpdate

---

Knowledge

Version=1 from JUN-2016 to 15-may-2020 fo_prefix=GFDLNFO_NIFTY_BNK_OPT_ index_prefix = "" q_prefix = ""

Version =2 from 16 May 2020 to Feb 28 2022

fo_prefix="GFDLNFO_CONTRACT_" index_prefix = "" eq_prefix = ""

Version =3 from Mar 01 2022 to current date fo_prefix="GFDLNFO_CONTRACT_" index_prefix = "GFDLCM_INDICES_" eq_prefix = "GFDLCM_STOCK_"

Pls Note Before Mar 01 2022 Index Spot Files and Cfut One Minute Data GDFL did not provided we have import from other source(only Index and future Data)

from Mar 01 2022 GDFL Provide all data

Important from this data we Have calculate Continous Future data from GDFL Data

Pls add This knowledge in claude.md also

Implementation

Corrected GDFL_VERSIONS in backend/nseieod/nseIEOD.py — the v2/v3 boundary moved from 2021-01-03/04 to 2022-02-28/03-01, per this updated knowledge:

version	Date range	fo_prefix	index_prefix	eq_prefix
1	2016-06-01 → 2020-05-14	GFDLNFO_NIFTY_BNK_OPT_	(none)	(none)
2	2020-05-15 → 2022-02-28	GFDLNFO_CONTRACT_	(none)	(none)
3	2022-03-01 → current	GFDLNFO_CONTRACT_	GFDLCM_INDICES_	GFDLCM_STOCK_

Verified against disk: csvs/2021/ has zero Index/Equity files (only GFDLNFO_CONTRACT_, ~193 files for the whole year) — confirming GDFL genuinely didn't provide Index/Equity until Mar 2022, matching this correction exactly. Re-running scratch/find_missing_gdfl_csv.py (no code changes needed -- it reads GDFL_VERSIONS directly) dropped its missing-file count for 2021-2024 from 810 to 236, eliminating the false "Index/Equity missing in all of 2021" noise the earlier (incorrect) boundary produced.

nseieod/CLAUDE.md rules 8 and 9 updated to match (also caught and fixed a stale v1/v2 boundary in rule 9's table that an earlier edit had missed).

Item	Status
GDFL_VERSIONS v2/v3 boundary corrected (2021-01 → 2022-03)	Done
nseieod/CLAUDE.md rules 8 + 9 updated	Done
find_missing_gdfl_csv.py re-verified against corrected boundary	Done (no code change needed)

Continues futures

Good Now I want convert Index and Stock Future in to Continues Future Format

for Example Nifty Continues futures Name with suffix "-" and Roman Letter of month index it will have continues future for update 3 months

Like NIFTY-I NIFTY-II NIFTY-III

you should write function (or class) in nseieod.py which accepts (fromdate (optional) and todate(optional) which copies date from futures with expiry date to continues future format if from date omitted from date would next working date of lastly updated continues future (with month index) if todate id omitted then it is last trading date of nse

Steps to convert

select distinct underly from scr_master where (typ=4 or typ=3) and monthindex=0 order by underly;

this give Underly list for C future 's Underlying

begin loop in this data Frame

Step-2 (Under Loop Level1) select masid,edate,underly,mname from scr_master where (typ=4 or typ=3) and monthindex=0 order by underly,edate; This data Frame masid of each future contracts

you can filter this data frame with Underline also edate should greater or equal to fromdate

Step-3

(Under Loop Level 2)

Once Underline and Edate filtered and sorted Edate

Select top 3 Expiry Dates along with their Masid(Set 1)

then select masid,edate,underly,mname from scr_master where monthindex>0 and monthindex<=3 and underly={Underly} order by monthindex (set 2)

1.)Now open data (from parquet file ) of masid from set 1 (first record) and copy data to masid and month index =1 and save to parquet file 2.)open data (from parquet file ) of masid from set 1 (second record) and copy data to masid and month index =2 and save to parquet file 3.)open data (from parquet file ) of masid from set 1 (third record) and copy data to masid and month index =3 and save to parquet file

do this process un till Trading date lower or equal to Expiry date (of First record)

Once Trading date crosses Expirydate Choose Next 3 Records from (set 1)

then continue the process till todate to complete underlying data

Step -4

then to same process for other Underlying (remaing)

Since it is lengthy process set some parameter that stops the process to verify the conversion

Implementation

Added build_cfut(fromdate, todate, underlyings, max_days) to backend/nseieod/nseIEOD.py as a module-level function (same style as pack_gdfl_range).

Run from backend/:

# Verify first 5 days for NIFTY+BANKNIFTY only
python -c "from nseieod.nseIEOD import build_cfut; build_cfut(fromdate='2022-03-01', todate='2022-03-31', max_days=5)"

# Full backfill for all underlyings
python -c "from nseieod.nseIEOD import build_cfut; build_cfut(fromdate='2022-03-01')"

# Resume (fromdate omitted -- auto-detects last CFut bar date)
python -c "from nseieod.nseIEOD import build_cfut; build_cfut()"

# One underlying
python -c "from nseieod.nseIEOD import build_cfut; build_cfut(fromdate='2022-03-01', underlyings=[1])"

How it works:

1. Step 1 — SELECT DISTINCT underly FROM scr_master WHERE (typ=4 OR typ=3) AND monthindex=0 gives all underlyings with real futures. Filtered by underlyings= param if set.

2. Step 2 — For each underlying, fetch all real contracts ordered by edate, and the CFut slots (monthindex=1/2/3) from scr_master.

3. fromdate auto-detect — if fromdate=None, scans Parquet files for the latest bar date already written for the near-month CFut slot (monthindex=1), then starts from the next NSE working day. Falls back to earliest contract edate if no data yet.

4. Step 3 (day loop) — for each trading day:

   • Picks top 3 contracts with edate >= trade_date (near / 2nd / 3rd month)
   • Reads the Parquet shard for that day once ({underly}_{IST_MONTH})
   • For each of the 3 slots: filters bars for real_masid, replaces masid with cfut_masid, writes back via merge-write (deduplicates on (masid, sdate))
   • When trade_date crosses the near-month expiry, the next iteration naturally picks the next set of 3 contracts (no explicit roll logic needed)

5. max_days — stops after N trading days per underlying for verification. Progress printed every 20 days.

Parquet layout (unchanged):

CFut bars land in the same monthly shard as the real contracts ({data_path}/{L}/{name}/{year}/{underly}_{MONTH_NAME}) but carry the CFut masid (e.g. 3 for NIFTY-I). Con_Price_Getter already reads these by filtering on masid, so CFut retrieval requires no changes.

Item	Status
build_cfut() in backend/nseieod/nseIEOD.py	Done
Auto fromdate from last CFut bar	Done
3-slot rolling window (near/2nd/3rd month)	Done
All underlyings or filtered by list	Done
max_days verification stop	Done
Parquet merge-write (dedup on masid+sdate)	Done

---

EodIEodsetup

Good from JUN 2026 we changed Data Vendor from GDFL to EODIEOD

Since it's format totally different from GDFL CSV format and we already has code for converting data from GDFL format to parquet basket Instead writing fresh code to convert for EODIEOD format we will convert data od EODIeod to GDFL format

You need to write a script that convert EodIEOD Data from GDFL format under folder \backend\nseieod\eodieod

let me to share What I observed from it's CSV

before that we have two stages of implementation 1.) convert whole June 2026 month to GDFL format 2.) Convert daily eodieod file (csv)

****** IMPORTANT BEFORE CONVERTING IT CHECK scr_master and scr_master,scr_nseeq_eod and scr_nsefo_eod TO ENSURE EODDATA DOWNLOADED IF IT NOT DOWNLOADED IT ASK USER TO DOWNLOAD IT WITH THAT IT SHOULD NOT PROCEED********* (add this in claude.md as a rule)

let look 4 major files

All these 4 files comes with as .rar format

1.) a.)NSE_Indices-2606.rar(June 2026) b.) NSE Indices IEOD - 10-07-2026.rar(daily sync) 2.) a.)IEOD_Option-2606.rar(June 2026) b.) Option_IEOD - 10-07-2026.rar(daily sync) 3.) a.)Cash_IEOD_Gold-2606.rar(June 2026) b.)Cash_IEOD - 10-07-2026.rar(daily sync) 4.) a.)IEOD_Future_Gold-2606.rar(June 2026) b.)Future_IEOD - 10-07-2026.rar(daily sync)

here after we assume (June 2026 ) files have all June 2026 Data and Files like (having date Like 10-07-2026 DD-MM-YYYY) have one minute data for particular day

Let use zoom in each files 1.) NSE_Indices file under folder we have many files each contains one minutes data for the period but names are different so we have convert the data using naming convention table

I have compiled a file in "D:\kss\stock\tradeentry-VPS\csvs\EODIEOD2026\eodieod_index_name_mapping.csv" Pls ingest this in namechanges table in duckdb (one time setup)

by using this namechanges table we can combine all these file in to single GDFL format file (Indices)

2.) Next We look at IEOD_Option it contains one minute data for an options contract

here is format

NSESymbol+YYMMMM(expiry)+Strike+OPtTyp(CE or PE)

since file name gives all information it is easy to combine all in one file

While converting this you should check whether it creates any shadow Expiry data.. for this Purpose only I have added rule that EOD records must be updated before converting EODIEOD Data

Though we can Allow Create any missing records in scr_master you should tell where are records going to inserted after the conversion

Also need to check whether any shadow Expiry Date(that should be avoided) 3.) Next EODIEOD Eq files(Cash_IEOD*.rar) When converting it should omit the records not in scr_nseeq_eod table

other is wise it straight forward

4.) Now it time to look FUT files it Crucial Part of conversion

In GDFL We have Generated Continues Future format

But in EODIEOD Provides only C-futures Data from which we have create normal Future Data (after checking namechanges)

also NIFTY_F1,NIFTY_F2,NIFTY_F3 should converted to NIFTY-I,NIFTY-II,NIFTY-III

from this records you have create Records for Normal Future (Here Only Month Expiries considered Not any weekly Expiries)

Be Carefull on shadow Expires

5.) here short Todo List on priority basis

i.) Ingest D:\kss\stock\tradeentry-VPS\csvs\EODIEOD2026\eodieod_index_name_mapping.csv in Name changes Duck _db Table ii.)Then check all 4 types of files presented iii.) check EOD Data updates in conversion date range iv.) the proceed convert as per instruction given above v.) All converted CSV should shored in CSV_PATH under EODIEOD/converted folder vi.) Write a short Script than ingest converted CSV in parquet basket

Implementation

New package backend/nseieod/eodieod/ converts EODIEOD data into GDFL format so the existing IEod packer and build_cfut() are reused unchanged. Key discovery: EODIEOD rows are already GDFL-shaped (<ticker>,<date>,<time>,<open>, <high>,<low>,<close>,<volume>[,<OI>]), so conversion = rename tickers + reformat date (MM/DD/YYYY -> DD/MM/YYYY) + combine per-instrument files into GDFL per-day files.

Run from backend/:

python -m nseieod.eodieod.run setup                       # todo i (one-time)
python -m nseieod.eodieod.run check   --month 2026-06     # prechecks only
python -m nseieod.eodieod.run convert --month 2026-06     # stage 1 (whole month)
python -m nseieod.eodieod.run convert --date  2026-07-10  # stage 2 (daily file)
python -m nseieod.eodieod.run ingest  --month 2026-06     # pack -> Parquet
#   then: build_cfut(fromdate='2026-06-01', todate='2026-06-30')  -> NIFTY-I/II/III

Modules & mapping to the todo list:

File	Responsibility
setup.py (i)	Ingest eodieod_index_name_mapping.csv into name_changes(source='EODIEOD') (skips unresolved symbols)
rario.py	Reads EODIEOD .rar files directly (WinRAR UnRAR.exe / 7-Zip; bulk-extract to temp, process, delete)
precheck.py (ii, iii)	4-.rar presence + tool check + mandatory EOD gate (refuses if scr_master/scr_nseeq_eod/scr_nsefo_eod don't cover every working day)
convert.py (iv, v)	4 converters -> GDFL day CSVs in CSV_PATH/EODIEOD/converted/, per-type progress bar, timing, full text report; shadow/new-contract report
ingest.py (vi)	Pack converted CSVs -> Parquet via IEod.pack() per segment + direct CFut ingest from EODIEOD {SYM}_F1/F2/F3
run.py	CLI orchestrator (setup/check/convert/ingest, --month/--date, --dry-run)

Conversion rules per type:

• Index — {eodieod_symbol}.csv (e.g. .NSEI) mapped via name_changes(EODIEOD) to canonical mname, emitted as {mname}.NSE_IDX (resolved by _resolve_index_masid). Unmapped symbols are dropped and reported.
• Option — {SYM}{YYMMDD}{STRIKE}{CE|PE} -> {SYM}{DDMMMYY}{STRIKE}{CE|PE} (e.g. NIFTY30JUN2625000CE). Regex constrains YY/MM/DD so symbols ending in digits (NIFTYNXT50) aren't mis-split; strike may be fractional (CANBK...136.8CE). Reports new contracts (auto-inserted at pack) and possible shadow expiries (rule 11).
• Cash/EQ — {SYM}.csv -> {SYM}.NSE; omits symbols not in scr_nseeq_eod.
• Future — {SYM}_F{1,2,3} continuous futures serve two outputs:
  • convert emits real monthly contracts {SYM}{DDMMMYY}FUT by mapping each day's slot to the n-th scr_master monthly expiry (typ IN (3,4), index=typ3 / stock=typ4).
  • ingest maps the same _F1/F2/F3 directly onto CFut masids (scr_master.monthindex 1/2/3 -> NIFTY-I/II/III). No build_cfut() — EODIEOD provides continuous futures natively (unlike GDFL), so reconstructing them would be redundant and could distort the series if the vendor's roll differs. build_cfut stays GDFL-era-only.

Reads from .rar directly: the 4 EODIEOD archives are placed under CSV_PATH/EODIEOD2026/ (name must contain the stage tag) and read in place — no manual extraction. Decimal-strike options and per-type console progress bars are supported.

Verified: rar-based check_files (all 4 archives + UnRAR.exe); real extraction of the Future .rar (641 files) + convert_futures (2.37M rows/9s); decimal-strike round-trip through IEod.generatemnamever2 (CROMPTON28JUL26292.5PE -> masid match); CFut masid mapping (NIFTY_F1/F2/F3 -> 3/3603/3604, BANKNIFTY -> 4/3598/4988) and _read_fut_df; version=4 prefix defaults + backward-compat of version=1/3 unchanged.

Origin-tagged filenames (version=4): converted/packed files now use EODIEOD* prefixes instead of GFDL* (e.g. EODIEODCM_INDICES_02062026.csv, not GFDLCM_INDICES_02062026.csv), so the data source is visible on disk even after packing. IEod.__init__ gained a version=4 profile (EODIEOD) alongside the existing GDFL versions 1-3 — same ticker parsing as v3 (else: # version 3 in generatemnamever2 already covers any version != 2), only the default fo_prefix/index_prefix/eq_prefix differ, driven by three new nseIEOD.py constants (EODIEOD_FO_PREFIX/EODIEOD_INDEX_PREFIX/ EODIEOD_EQ_PREFIX) that convert.py's _Emitter and ingest.py's IEod() construction both import — single source of truth, can't drift apart. index_prefix/eq_prefix also became real constructor parameters (previously hardcoded inside __init__, only overridable by poking the instance attribute after construction, which ingest.py used to do). version=4 is deliberately not added to GDFL_VERSIONS/pack_gdfl_range() — that automation is a date-driven scan for real GDFL CSVs, unrelated to EODIEOD's manually-invoked flow. Full detail: docs/developer/packages/nseieod/nseieod-main.md.

Also noted per project leader: EODIEOD's actual roll-forward policy for {SYM}_F1/F2/F3 (whether it switches to the next contract exactly on expiry, like convert_futures's scr_master-expiry-based assumption, or a few days early) cannot be verified for the June-2026 window — no independent expired-contract reference data is available for that month. Deferred to a spot-check once daily EODIEOD syncs are running against fresh (not-yet-expired) contracts, where the roll can be observed directly instead of inferred.

Status: Implemented in backend/nseieod/eodieod/ + nseieod/CLAUDE.md rules 9 (cross-reference) and 12.

Item	Status
i. eodieod_index_name_mapping.csv -> name_changes(EODIEOD) (setup.py)	Done
ii. 4 .rar presence + tool check (precheck.check_files)	Done
iii. EOD-updated gate (precheck.check_eod, hard-stop)	Done
iv. 4 converters (Index/Option/Cash/Future) -> GDFL-shaped CSVs	Done
v. Output to CSV_PATH/EODIEOD/converted/ + full text report	Done
vi. Ingest converted CSV -> Parquet (ingest.py, IEod version=4)	Done
Reads .rar directly (rario.py, no manual extraction)	Done
Decimal-strike options + per-type progress bars	Done
Direct CFut ingest (F1/F2/F3 -> NIFTY-I/II/III; no build_cfut)	Done
Origin-tagged filenames (EODIEOD* prefix, IEod version=4)	Done
Shadow-expiry / new-contract reporting (rule 11)	Done
nseieod/CLAUDE.md rule 12 (EOD-before-convert)	Done

---

EODIEOD converter

Good it is time write a single scripts that converts EODIEOF files then ingests in parquet bucket then fixdata.filldata for masid 1,2,3,4 and 987106 and report any big misses in to log to report Data vendor lastly give CLI

Implementation

New backend/nseieod/eodieod/pipeline.py — one call does convert → ingest → fix_data.filldata() (masid 1, 2, 3, 4, 987106 by default = NIFTY, BANKNIFTY, NIFTY-I, BANKNIFTY-I, India VIX — all already-registered liquid_scripts roots) → a vendor-issue report of any "big miss".

Run from backend/:

python -m nseieod.eodieod.run pipeline --month 2026-06
python -m nseieod.eodieod.run pipeline --date  2026-07-10
python -m nseieod.eodieod.run pipeline --month 2026-06 --dry-run          # stop after convert
python -m nseieod.eodieod.run pipeline --month 2026-06 --masids 1,2      # override the masid list

"Big miss" definition — reused the project's own existing threshold rather than inventing a new one: a whole trading day with zero bars, or an intraday gap of more than 20 consecutive missing bars (fill flag < -3, nseieod/ CLAUDE.md rule 7 — "low-confidence, should be replaced with another vendor's data"). filldata(apply=True) already patches these with a synthetic previous-day-pattern fill so the store isn't literally empty, but a gap that size means EODIEOD itself failed to deliver real data for that stretch — that is what gets escalated to the vendor-issue report, separate from the small, self-healed gaps (1-bar carry, 2-5 bar interpolation, 6-20 bar pattern) that don't need reporting anywhere.

Two output files per run, both under CSV_PATH/EODIEOD/converted/:

• pipeline_log_{tag}_{timestamp}.txt — the full run's console output (every stage, every masid's detailed fix_data dashboard), via fix_data. _tee_stdout.
• vendor_report_{tag}_{timestamp}.txt — just the filtered "big miss" rows (masid, date, time range, bar count, fill note), formatted for handing directly to the data vendor.

Verified: dry-run against the real .rar files (EOD gate pass → convert all 4 types, 0 unparsed, 0 shadow expiries, correctly stops before ingest); filldata() for all 5 default masids against already-ingested, already-clean June 2026 data (0 clusters each, matching the earlier standalone fix_data. check result); the report-writing logic itself with synthetic big-miss data (a 30-bar gap and a whole-missing day both correctly flagged and formatted; a 5-bar gap — below the 20-bar threshold — correctly excluded).

Fixed one bug surfaced by wiring convert.py's _Progress bar under fix_data ._tee_stdout: it assumed sys.stdout.isatty() always exists, which crashed under the tee wrapper (no isatty attribute). Now defensive: getattr(sys. stdout, "isatty", lambda: False)().

Item	Status
Single script: convert → ingest → filldata(masid list) (pipeline.py)	Done
Default masid list 1,2,3,4,987106 (India VIX confirmed already registered)	Done
Big-miss definition reuses rule 7's filled < -3 threshold	Done
Vendor-issue report file (filtered, big misses only)	Done
Full run log file (tee'd console output)	Done
CLI (run.py pipeline subcommand, --dry-run/--masids)	Done
_Progress.isatty() robustness fix (needed for the tee wrapper)	Done

Correction — purpose is DAILY update; sequential guard + update CLI

Follow-up from the project leader: the pipeline's actual purpose is a daily update run once EODIEOD's sync for that date is ready, not an arbitrary-date tool — it must refuse to convert any date while an earlier one is still unfilled, and must verify all 4 source files are present.

What was found: check_files()'s 4-file check was already correct in isolation. The real gap was that nothing stopped a later, available date from being processed while an earlier date had no source .rar at all — confirmed for real, not hypothetical: csvs/EODIEOD2026/ has daily files for 01-07-2026 and 10-07-2026 but none for 02–09-07-2026. convert --date 10-07-2026 would have silently stranded that gap forever.

Fix:

• New sequential guard (pipeline.check_sequential()) — refuses unless the requested date is exactly the next pending NSE working day after the checkpoint. pipeline --date/--month now enforce it by default (--no-sequential to override for a deliberate backfill).
• New pipeline.run_daily_update() / CLI run.py update (no date args) — the actual daily-update entry point. Auto-resolves the checkpoint, processes pending working days one at a time, stops cleanly (not an error) the moment a day's 4 files aren't present yet.
• Bug found and fixed along the way: the checkpoint mechanism (fix_data._record_checked_till()) called update_checked_till(root=root, ...), but that function's parameter is actually named masid — every call raised TypeError, silently swallowed by a bare except: pass. checked_till had never been successfully recorded. Fixed (masid=root); all 3 anchor roots (NIFTY, BANKNIFTY, India VIX) manually baselined to 2026-06-30 afterward, since June was already independently verified complete multiple times.

Run for July (project leader will run this themselves):

cd backend
python -m nseieod.eodieod.run update

No --date/--month needed — it will process 2026-07-01 (files present), then correctly stop at 2026-07-02 reporting it as the next pending date with no .rar files found yet.

Verified (read-only, no real conversion executed):

• get_checkpoint() → 2026-06-30; next_pending_date() → 2026-07-01
• check_sequential(2026-07-10) → refuses, correctly citing 2026-07-01 as the actual next pending date
• check_sequential(2026-07-01) → allows
• check_files() independently confirmed: 01-07-2026 and 10-07-2026 OK, 02-07-2026 correctly flagged as fully missing (all 4 types)

Item	Status
Sequential guard (check_sequential, get_checkpoint, next_pending_date)	Done
pipeline --date/--month enforce it by default (--no-sequential override)	Done
run_daily_update() + CLI run.py update (no date args, auto-catchup)	Done
_record_checked_till() keyword-argument bug (root= → masid=)	Done
Anchor roots (1, 2, 987106) baselined to 2026-06-30	Done
Verified against real July gap (02–09-07-2026 missing) — guard blocks correctly	Done
Live July run	Not run — reserved for the project leader

Structured logging — loguru, no Sentry, no text-based reports

Follow-up: TradeEntry is growing into an algo backtesting + execution + accounts-management platform with no real log management (just ad hoc .txt report files). Discussed the options (Sentry, structlog vs loguru, self-hosted vs SaaS, VPS-crash-specific logging) — landed on loguru only, lightweight, no Sentry for this stage of the project.

Added: Config.LOG_PATH (LOCAL_LOG_PATH/VPS_LOG_PATH, defaults to backend/logs/) and backend/app/logging_config.py — one shared logger (loguru), console + daily-rotated JSON-lines file (30-day retention, enqueue=True for the parallel Parquet writes from rule 10a). See backend/CLAUDE.md rule 3.

Then removed every standalone text-report file (project leader's call — console print()s stay, but no more .txt artifacts):

• pipeline.py's vendor_report_{tag}_{ts}.txt and pipeline_log_{tag}_{ts}.txt (the fix_data._tee_stdout-based full-run tee)
• convert.py's convert_report_{tag}_{ts}.txt (_write_report_file)
• fix_data.py's CLI fix_data_{check,filldata}_*.txt (_tee_stdout/_Tee/ _report_path, all now dead code, removed)

Every category those files used to hold is now a structured log event instead (same information, queryable instead of a flat file): vendor_data_gap (per gap, includes the fill-flag note), eodieod_convert_complete

• one event per non-empty category (cash_symbols_omitted, index_symbols_dropped, option_filenames_unparsed, option_new_expiries, option_possible_shadow_expiries, ...), fix_data_scan_gaps_found + per-masid fix_data_masid_gaps, fix_data_intraday_fill, fix_data_whole_day_restore, fix_data_could_not_restore.

Verified: fix_data.check() against already-clean June 2026 data — no new .txt file created, correct fix_data_scan_no_gaps event logged; _log_convert_report/vendor-gap logging tested with synthetic data (all categories fire at the right log level, note field correctly carries the fill-flag annotation that used to be in the txt report).

Item	Status
Config.LOG_PATH + app/logging_config.py (loguru, console + JSON-lines, no Sentry)	Done
Removed pipeline.py text reports (vendor + full-run log)	Done
Removed convert.py text report (_write_report_file)	Done
Removed fix_data.py CLI text reports (_tee_stdout/_Tee/_report_path)	Done
Structured log events replacing every removed report's content	Done
.gitignore updated for backend/logs//*.jsonl	Done