https://stafforini.com/notes/Hugo -- gohugo.ioenWed, 15 Apr 2026 00:00:00 +0000https://stafforini.com/notes/situational-awareness-lp/Fri, 27 Feb 2026 00:00:00 +0000https://stafforini.com/notes/situational-awareness-lp/<. This approach has significant limitations, however: the copycat sees each portfolio only after it is disclosed, not when the fund actually trades into it;[^fn:1] 13F filings exclude short positions, foreign-listed securities, and non-equity assets; and, for options, filings identify the underlying security but omit strike prices, expirations, and premiums. Still, the loss of fidelity may be acceptable if one is sufficiently bullish on the fund's strategy and wants a simple way to get exposure to its public equity bets. Backtesting the strategy {#backtesting-the-strategy} A trader following this strategy would rebalance to each new 13F on its filing date — buying the disclosed portfolio at that day’s closing prices, weighting each position by its reported dollar value — and hold unchanged until the next filing. Backtesting the rule is then mostly mechanical: replay the rebalances across all available filings and compound the period returns. One wrinkle complicates the replay. 13F filings report options only partially: they name the underlying security and give a dollar value, but omit strike, expiration, and premium. Since that is not enough information to reconstruct the actual option positions, the script below reports the backtest under two modes, labelled _equity proxy_ and _option proxy_. Both are explained in detail after the script. The final period runs from the most recent filing to today, so re-running the script updates the last row automatically.[^fn:2]

Code

```python ── SA LP 13F data fetcher ───────────────────────────────────────── Fetches all 13F-HR filings from SEC EDGAR for Situational Awareness LP, parses the infotable XML, and resolves CUSIPs to tickers. Output: JSON with one entry per quarterly filing, each containing the filing date, quarter label, and a list of holdings. Caches results in CACHE_DIR to avoid redundant SEC requests. import urllib.request, re, json, sys, time, os, xml.etree.ElementTree as ET from datetime import datetime ── Configuration (update these for your environment) ────────────── SEC_UA = os.environ.get( 'SEC_USER_AGENT', 'stafforini.com situational-awareness-lp research; contact via stafforini.com') CACHE_DIR = os.path.expanduser('~/.cache') CIK = '2045724' BASE = f'https://www.sec.gov/Archives/edgar/data/{CIK}' NS = {'ns': 'http://www.sec.gov/edgar/document/thirteenf/informationtable'} CACHE = os.path.join(CACHE_DIR, 'sa-lp-13f.json') CUSIP_TICKER = { '038169207': 'APLD', '05614L209': 'BW', '09173B107': 'BITF', '093712107': 'BE', '093712AH0': 'BE', '11135F101': 'AVGO', '12514G108': 'CIFR', '17253J106': 'CIFR', '17253JAA4': 'CIFR', '18452B209': 'CLSK', '19247G107': 'COHR', '21037T109': 'CEG', '21873S108': 'CRWV', '21874A106': 'CORZ', '26884L109': 'EQT', '36168Q104': 'GLXY', '36317J209': 'GLXY', '44282L109': 'HUT', '44812J104': 'HUT', '456788108': 'INFY', '458140100': 'INTC', '49338L103': 'KRC', '49427F108': 'KRC', '53115L104': 'LBRT', '55024U109': 'LITE', '55024UAD1': 'LITE', '573874104': 'MRVL', '577933104': 'MRVL', '593787101': 'MU', '593787105': 'MU', '595112103': 'MU', '607828100': 'MOD', '67066G104': 'NVDA', '683344105': 'ONTO', '68340J108': 'ONTO', '73933G202': 'PSIX', '73933H100': 'PSIX', '743344109': 'PUMP', '74347M108': 'PUMP', '76754A103': 'RIOT', '767292105': 'RIOT', '80004C200': 'SNDK', '80106M109': 'SNDK', '83418M103': 'SEI', '87422Q109': 'TLN', '87425V106': 'TLN', '874039100': 'TSM', '89854H102': 'TSEM', '92189F106': 'SMH', '92189F676': 'SMH', '92535P101': 'VRT', '92537N108': 'VRT', '92840M102': 'VST', '958102105': 'WDC', '958102AT2': 'WDC', '98321C108': 'WYFI', 'G1110V104': 'BITF', 'G1189L107': 'BTDR', 'G11448100': 'BTDR', 'G7945J104': 'STX', 'G7997R103': 'STX', 'G96115103': 'WYFI', 'M87915274': 'TSEM', 'Q4982L109': 'IREN', } def fetch(url, timeout=10): time.sleep(0.5) req = urllib.request.Request(url, headers={'User-Agent': SEC_UA}) with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.read() def find_infotable_filename(acc): """Discover the infotable XML filename for a filing via EFTS, then -index.htm.""" EFTS search (fast, reliable) try: efts = f'https://efts.sec.gov/LATEST/search-index?q=%22{acc}%22' data = json.loads(fetch(efts)) for hit in data.get('hits', {}).get('hits', []): doc_id = hit['_id'] # format: "accession:filename" filename = doc_id.split(':', 1)[1] if ':' in doc_id else '' if filename.endswith('.xml') and 'primary_doc' not in filename: return filename except Exception: pass Fallback: filing index page acc_path = acc.replace('-', '') try: html = fetch(f'{BASE}/{acc_path}/{acc}-index.htm').decode() for href in re.findall(r'href="([^"]*\.xml)"', html): fn = href.split('/')[-1] if fn != 'primary_doc.xml' and 'xslForm' not in href: return fn except Exception: pass return None def parse_infotable(xml_data): root = ET.fromstring(xml_data) holdings = [] for info in root.findall('.//ns:infoTable', NS): cusip = info.findtext('ns:cusip', '', NS).strip() value = int(info.findtext('ns:value', '0', NS)) putcall = info.findtext('ns:putCall', '', NS).strip().lower() ticker = CUSIP_TICKER.get(cusip, '') if not ticker: issuer = info.findtext('ns:nameOfIssuer', '', NS) print(f"WARNING: Unknown CUSIP {cusip} ({issuer})", file=sys.stderr) ticker = f"UNKNOWN_{cusip}" pos_type = 'put' if putcall == 'put' else 'call' if putcall == 'call' else 'long' holdings.append({"ticker": ticker, "type": pos_type, "value": value}) return holdings def quarter_from_filing_date(fdate): d = datetime.strptime(fdate, '%Y-%m-%d') m, y = d.month, d.year if m<= 3:= return= f'Q4_{y-1}',= f'{y-1}-12-31'= elif= m= <=6: return= f'Q1_{y}',= f'{y}-03-31'= elif= m= <=9: return= f'Q2_{y}',= f'{y}-06-30'= else:= return= f'Q3_{y}',= f'{y}-09-30'= def= load_cache():= if= os.path.exists(CACHE):= with= open(CACHE)= as= f:= return= json.load(f)= return= {"filings":= []}= def= save_cache(data):= os.makedirs(os.path.dirname(CACHE),= exist_ok=True) with= open(CACHE,= 'w')= as= f:= json.dump(data,= f)= cached=load_cache() cached_quarters={f["quarter"] for= f= in= cached["filings"]}= result={"filings": list(cached["filings"])}= try:= subs_url=f'https://data.sec.gov/submissions/CIK{CIK.zfill(10)}.json' subs=json.loads(fetch(subs_url)) recent=subs['filings']['recent'] accessions=sorted( [(recent['filingDate'][i],= recent['accessionNumber'][i])= for= i= in= range(len(recent['form']))= if= recent['form'][i]== '13F-HR'])= for= fdate,= acc= in= accessions:= quarter,= quarter_end=quarter_from_filing_date(fdate) if= quarter= in= cached_quarters:= continue= filename=find_infotable_filename(acc) if= not= filename:= print(f"Could= not= find= infotable= for= {acc}",= file=sys.stderr) continue= acc_path=acc.replace('-', '')= xml=fetch(f'{BASE}/{acc_path}/{filename}') holdings=parse_infotable(xml) result["filings"].append({= "quarter":= quarter,= "quarter_end":= quarter_end,= "filing_date":= fdate,= "holdings":= holdings})= result["filings"].sort(key=lambda f:= f["filing_date"])= save_cache(result)= except= Exception= as= e:= if= result["filings"]:= print(f"SEC= fetch= error= ({e});= using= cache",= file=sys.stderr) else:= raise= return= json.dumps(result)= ```= ```python import json import yfinance as yf import pandas as pd from datetime import datetime, timedelta import numpy as np import requests import time import os import warnings warnings.filterwarnings('ignore') Parse data from the scraper block parsed = json.loads(data) if isinstance(data, str) else data filings = parsed["filings"] Build internal structures filing_dates = {f["quarter"]: f["filing_date"] for f in filings} quarter_end_dates = {f["quarter"]: f["quarter_end"] for f in filings} quarters = [f["quarter"] for f in filings] Convert holdings list to dict keyed by quarter. Multiple positions in the same ticker with different types are aggregated by value per (ticker, type) pair. holdings = {} for f in filings: positions = {} for h in f["holdings"]: ticker = h["ticker"] pos_type = h["type"] value = h["value"] key = (ticker, pos_type) positions[key] = positions.get(key, 0) + value holdings[f["quarter"]] = positions def _extract_close_series(df, ticker): """Extract a single close-price series from a yfinance result.""" if df.empty: return pd.Series(dtype=float) if isinstance(df.columns, pd.MultiIndex): if 'Close' not in df.columns.get_level_values(0): return pd.Series(dtype=float) close = df['Close'] if isinstance(close, pd.DataFrame): if ticker in close.columns: series = close[ticker] elif len(close.columns) == 1: series = close.iloc[:, 0] else: return pd.Series(dtype=float) else: series = close elif 'Close' in df.columns: series = df['Close'] if isinstance(series, pd.DataFrame): series = series.iloc[:, 0] else: return pd.Series(dtype=float) return pd.to_numeric(series, errors='coerce').dropna() def _download_close_series(ticker, start, end): """Download one ticker's close series; used to repair flaky batch misses.""" df = yf.download(ticker, start=start, end=end, progress=False, auto_adjust=True) return _extract_close_series(df, ticker) def get_prices(tickers, dates): """Fetch close prices for tickers on specific dates.""" unique_tickers = sorted(set(tickers)) all_dates = [datetime.strptime(d, '%Y-%m-%d') for d in dates] start = min(all_dates) - timedelta(days=5) end = max(all_dates) + timedelta(days=5) df = yf.download(unique_tickers, start=start, end=end, progress=False, auto_adjust=True) yf.download returns MultiIndex columns (metric, ticker) for multiple tickers if df.empty: close = pd.DataFrame() elif isinstance(df.columns, pd.MultiIndex) and 'Close' in df.columns.get_level_values(0): close = df['Close'].copy() elif 'Close' in df.columns: close = df[['Close']].copy() close.columns = unique_tickers else: close = pd.DataFrame() prices = {} for ticker in unique_tickers: if ticker in close.columns: series = pd.to_numeric(close[ticker], errors='coerce').dropna() else: series = pd.Series(dtype=float) if series.empty: series = _download_close_series(ticker, start, end) if series.empty: continue prices[ticker] = {} for date_str in dates: target = pd.Timestamp(datetime.strptime(date_str, '%Y-%m-%d')) after = series[series.index >= target] if not after.empty: prices[ticker][date_str] = float(after.iloc[0]) else: before = series[series.index<= target]= if= not= before.empty:= prices[ticker][date_str]=float(before.iloc[-1]) return= prices= def= _price_on_or_after(px_by_date,= target_date):= """Return= (date,= price)= for= the= first= available= price= on/after= target."""= if= not= px_by_date:= return= None= dates=sorted(d for= d= in= px_by_date= if= d=>= target_date) if not dates: return None d = dates[0] return d, px_by_date[d] def _price_on_or_before(px_by_date, target_date): """Return (date, price) for the last available price on/before target.""" if not px_by_date: return None dates = sorted(d for d in px_by_date if d<= target_date)= if= not= dates:= return= None= d=dates[-1] return= d,= px_by_date[d]= def= _period_price_pair(px_by_date,= start_date,= end_date):= """Return= start/end= prices= for= a= period= using= sensible= boundary= alignment."""= start=_price_on_or_after(px_by_date, start_date)= end=_price_on_or_before(px_by_date, end_date)= if= start= is= None= or= end= is= None:= return= None= start_actual,= p0=start end_actual,= p1=end if= end_actual= <= start_actual:= return= None= return= start_actual,= end_actual,= p0,= p1= def= _option_position_key(ticker,= pos_type):= return= (ticker,= pos_type)= def= _linear_underlying_sign(pos_type):= """Direction= when= option= rows= are= converted= to= underlying= equity= exposure."""= return= -1= if= pos_type== 'put'= else= 1= def= compute_return(positions,= prices,= start_date,= end_date,= mode='equity_only' ,= option_prices=None): """Compute= portfolio= return= between= two= dates.= The= 13F= value= for= an= option= row= is= treated= as= underlying= notional,= not= option= premium.= Option= contracts= are= sized= from= that= notional,= but= the= portfolio= denominator= is= estimated= deployed= capital:= stock= value= plus= option= premium= cost.= This= avoids= treating= the= gap= between= option= notional= and= option= premium= as= cash.= In= 'full'= mode,= every= option= row= requires= a= MarketData= price= series;= missing= data= raises= rather= than= falling= back.= """= total_cost=0 portfolio_pnl=0 for= (ticker,= pos_type),= value= in= positions.items():= is_option=pos_type in= ('call',= 'put')= stock_px=prices.get(ticker) if= mode== 'equity_only':= if= pos_type= not= in= ('long',= 'call',= 'put'):= continue= pair=_period_price_pair(stock_px, start_date,= end_date)= if= pair= is= None:= continue= start_actual,= end_actual,= p0,= p1=pair if= p0== 0:= continue= stock_ret=(p1 -= p0)= /= p0= total_cost= +=value portfolio_pnl= +=value *= _linear_underlying_sign(pos_type)= *= stock_ret= continue= if= is_option:= opt_key=_option_position_key(ticker, pos_type)= opt_px=option_prices.get(opt_key) if= option_prices= else= None= if= not= opt_px:= raise= RuntimeError(= f"No= MarketData= option= prices= for= {opt_key}= in= period= "= f"{start_date}..{end_date}")= pair=_period_price_pair(opt_px, start_date,= end_date)= if= pair= is= None:= raise= RuntimeError(= f"MarketData= option= price= series= for= {opt_key}= does= not= "= f"cover= {start_date}..{end_date}")= start_actual,= end_actual,= opt_p0,= opt_p1=pair stock_start=_price_on_or_after(stock_px, start_actual)= if= stock_start= is= None= or= stock_start[1]= <=0: stock_start=_price_on_or_after(stock_px, start_date)= if= stock_start= is= None= or= stock_start[1]= <=0: raise= RuntimeError(= f"No= underlying= price= for= {ticker}= at= {start_date}")= p0,= p1=opt_p0, opt_p1= underlying_p0=stock_start[1] if= p0= <=0 or= underlying_p0= <=0: continue= position_cost=value *= (p0= /= underlying_p0)= position_pnl=value *= ((p1= -= p0)= /= underlying_p0)= else:= pair=_period_price_pair(stock_px, start_date,= end_date)= if= pair= is= None:= continue= start_actual,= end_actual,= p0,= p1=pair if= p0== 0:= continue= stock_ret=(p1 -= p0)= /= p0= position_cost=value position_pnl=value *= stock_ret= if= position_cost= <=0: continue= total_cost= +=position_cost portfolio_pnl= +=position_pnl return= portfolio_pnl= /= total_cost= if= total_cost= else= None= def= annualize(ret,= days):= """Annualize= a= return= over= a= given= number= of= calendar= days."""= if= ret= is= None= or= days= <=0: return= None= return= (1= += ret)= **= (365.25= /= days)= -= 1= def= fmt(ret):= return= f"{ret= *= 100:+.2f}%"= if= ret= is= not= None= else= "N/A"= Collect= all= tickers= and= dates= all_tickers=set() for= positions= in= holdings.values():= for= (ticker,= _)= in= positions:= all_tickers.add(ticker)= all_tickers.add('SPY')= today=datetime.now().strftime('%Y-%m-%d') first_date=filing_dates[quarters[0]] all_dates=set(filing_dates.values()) |= set(quarter_end_dates.values())= |= {today}= prices=get_prices(sorted(all_tickers), sorted(all_dates))= Resolve= `today`= to= the= actual= last= available= closing= date.= yfinance= may= not= have= data= for= today= (market= still= open= or= holiday),= so= we= look= up= what= date= SPY's= price= actually= corresponds= to.= def= _resolve_price_date(prices,= requested_date):= """Return= the= actual= trading= date= of= the= price= stored= under= requested_date."""= ref='SPY' if= 'SPY'= in= prices= else= next(iter(prices),= None)= if= not= ref= or= requested_date= not= in= prices[ref]:= return= requested_date= target_price=prices[ref][requested_date] Re-download= a= small= window= to= find= the= real= date= of= this= price= start=datetime.strptime(requested_date, '%Y-%m-%d')= -= timedelta(days=10) end=datetime.strptime(requested_date, '%Y-%m-%d')= += timedelta(days=5) df=yf.download(ref, start=start, end=end, progress=False, auto_adjust=True) if= df.empty:= return= requested_date= if= isinstance(df.columns,= pd.MultiIndex):= close=df['Close'][ref].dropna() elif= 'Close'= in= df.columns:= close=df['Close'].dropna() else:= close=df.iloc[:, 0].dropna()= for= dt,= px= in= close.items():= val=float(px.iloc[0]) if= isinstance(px,= pd.Series)= else= float(px)= if= abs(val= -= target_price)= <= 0.01:= ts=dt[0] if= isinstance(dt,= tuple)= else= dt= return= pd.Timestamp(ts).strftime('%Y-%m-%d')= return= requested_date= today_resolved=_resolve_price_date(prices, today)= if= today_resolved= !=today: for= ticker= in= prices:= if= today= in= prices[ticker]:= prices[ticker][today_resolved]=prices[ticker].pop(today) today=today_resolved def= download_daily(tickers,= start_date,= end_date):= """Download= daily= close= prices= from= yfinance,= handling= MultiIndex.= Dates= are= 'YYYY-MM-DD'= strings.= Adds= a= small= buffer= for= trading-day= alignment."""= tickers_sorted=sorted(tickers) start=datetime.strptime(start_date, '%Y-%m-%d')= -= timedelta(days=5) end=datetime.strptime(end_date, '%Y-%m-%d')= += timedelta(days=5) df=yf.download(tickers_sorted, start=start, end=end, progress=False, auto_adjust=True) if= df.empty:= close=pd.DataFrame() elif= isinstance(df.columns,= pd.MultiIndex)= and= 'Close'= in= df.columns.get_level_values(0):= close=df['Close'].copy() elif= 'Close'= in= df.columns:= close=df[['Close']].copy() close.columns=tickers_sorted else:= close=pd.DataFrame() for= ticker= in= tickers_sorted:= if= ticker= in= close.columns= and= not= close[ticker].dropna().empty:= continue= series=_download_close_series(ticker, start,= end)= if= not= series.empty:= close[ticker]=series return= close.sort_index()= --= Historical= option= prices= via= MarketData= --------------------------------= OPTION_CACHE_DIR=os.path.expanduser('~/My Drive/notes/.sa-lp-option-cache')= _MD_BASE='https://api.marketdata.app/v1' _MD_RATE_DELAY=0.15 OPTION_CACHE_COLUMNS=[ 'date',= 'selected_on',= 'option_type',= 'symbol',= 'strike',= 'expiry',= 'delta',= 'price']= Default= contract= selection= parameters.= The= option= proxy= picks= a= contract= matching= option= type,= with= expiry= between= min_days= and= max_days= of= the= period= start,= and= |delta|= closest= to= delta_target.= When= the= chain= is= sparse,= the= achieved= |delta|= may= be= far= from= the= target;= the= sensitivity= block= reports= achieved= |delta|= so= this= is= visible= rather= than= silent.= OPTION_DELTA=0.15 EXPIRY_MIN_DAYS=270 #= ~9= months= EXPIRY_MAX_DAYS=456 #= ~15= months= def= _normalize_option_type(option_type):= option_type=str(option_type).lower() if= option_type= not= in= ('call',= 'put'):= raise= ValueError(f"Unsupported= option= type:= {option_type}")= return= option_type= def= _empty_option_cache():= return= pd.DataFrame(columns=OPTION_CACHE_COLUMNS) def= _option_cache_path(ticker,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Return= the= cache= CSV= path= for= (ticker,= type,= delta_target,= window).= When= the= parameter= triple= equals= the= baseline= (0.15,= 270-456= days),= the= historical= filename= ``TICKER-TYPE.csv``= is= used= so= the= main-backtest= cache= is= reused= automatically.= Any= non-baseline= combo= lives= in= a= separate= ``TICKER-TYPE-d-e-.csv`` file so a sensitivity sweep never pollutes the baseline cache (which the portfolio calculator reads to pick the representative contract for the current filing). """ option_type = _normalize_option_type(option_type) is_baseline = ( abs(delta_target - OPTION_DELTA)< 1e-9= and= min_days== EXPIRY_MIN_DAYS= and= max_days== EXPIRY_MAX_DAYS)= if= is_baseline:= return= os.path.join(OPTION_CACHE_DIR,= f'{ticker}-{option_type}.csv')= return= os.path.join(= OPTION_CACHE_DIR,= f'{ticker}-{option_type}-d{delta_target:g}-e{min_days}-{max_days}.csv')= def= _load_option_cache(ticker,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Load= cached= MarketData= rows= for= a= ticker/type/target/window.= Returns= DataFrame= or= empty."""= option_type=_normalize_option_type(option_type) path=_option_cache_path(ticker, option_type,= delta_target,= min_days,= max_days)= if= not= os.path.exists(path):= return= _empty_option_cache()= df=pd.read_csv(path) if= df.empty:= return= _empty_option_cache()= for= col= in= OPTION_CACHE_COLUMNS:= if= col= not= in= df.columns:= df[col]=np.nan for= col= in= ('date',= 'selected_on'):= df[col]=pd.to_datetime( df[col],= errors='coerce' ).dt.strftime('%Y-%m-%d')= df['option_type']=df['option_type'].fillna(option_type).str.lower() cache=df[OPTION_CACHE_COLUMNS].copy() cache=cache[cache['option_type'] == option_type].copy()= cache.dropna(subset=['date'], inplace=True) for= col= in= ('strike',= 'delta',= 'price'):= cache[col]=pd.to_numeric(cache[col], errors='coerce' )= cache.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) cache.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) return= cache[OPTION_CACHE_COLUMNS]= def= _save_option_cache(ticker,= option_type,= df,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Persist= typed= option= cache= to= CSV."""= option_type=_normalize_option_type(option_type) os.makedirs(OPTION_CACHE_DIR,= exist_ok=True) path=_option_cache_path(ticker, option_type,= delta_target,= min_days,= max_days)= if= df.empty:= df=_empty_option_cache() else:= df=df.copy() df['option_type']=option_type for= col= in= OPTION_CACHE_COLUMNS:= if= col= not= in= df.columns:= df[col]=np.nan df.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) df.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) df.to_csv(path,= index=False) def= _contract_window(ref_date_str,= min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): ref=datetime.strptime(ref_date_str, '%Y-%m-%d')= return= ref= += timedelta(days=min_days), ref= += timedelta(days=max_days) def= _contract_from_cache_row(row,= ref_date_str,= option_type,= min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): option_type=_normalize_option_type(option_type) if= str(row.get('option_type',= option_type)).lower()= !=option_type: return= None= lo,= hi=_contract_window(ref_date_str, min_days,= max_days)= try:= exp=datetime.strptime(str(row['expiry']), '%Y-%m-%d')= except= (KeyError,= TypeError,= ValueError):= return= None= if= not= (lo= <=exp <=hi): return= None= strike=_safe_float(row.get('strike')) delta=_safe_float(row.get('delta')) price=_safe_float(row.get('price')) if= strike= is= None= or= delta= is= None= or= price= is= None= or= price= <=0: return= None= return= {= 'selected_on':= row.get('selected_on'),= 'option_type':= option_type,= 'symbol':= row.get('symbol'),= 'strike':= strike,= 'expiry':= str(row['expiry']),= 'delta':= delta,= 'price':= price,= }= def= _select_cached_contract(cache,= option_type,= ref_date_str,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS, require_selected=False): rows=cache[(cache['date'] == ref_date_str)= &= (cache['option_type']== option_type)]= selected_rows=rows[rows['selected_on'] == ref_date_str]= if= not= selected_rows.empty:= rows=selected_rows elif= require_selected:= rows=selected_rows candidates=[] for= _,= row= in= rows.iterrows():= contract=_contract_from_cache_row(row, ref_date_str,= option_type,= min_days,= max_days)= if= contract:= candidates.append(contract)= if= not= candidates:= return= None= candidates.sort(key=lambda x:= abs(abs(x['delta'])= -= delta_target))= return= candidates[0]= def= _parse_option_price(contract):= """Extract= a= mark= price= from= an= option= contract= record."""= mid=_safe_float(contract.get('mid')) if= mid= and= mid=> 0: return mid bid = _safe_float(contract.get('bid')) ask = _safe_float(contract.get('ask')) last = _safe_float(contract.get('last')) if bid and ask and bid > 0 and ask > 0: return (bid + ask) / 2 if last and last > 0: return last return None def _safe_float(val): try: out = float(val) if np.isnan(out): return None return out except (TypeError, ValueError): return None def _marketdata_key(): """Return the MarketData API key, or None if unavailable. Resolution order: 1. ``MARKETDATA_KEY`` / ``MARKETDATA_API_KEY`` environment variables. 2. ``pass env/marketdata-token`` (local ``pass`` store). The result is memoised on the function object so repeated lookups during a sweep do not reshell. Fetch helpers raise themselves when called without a key, so a fully cached run still succeeds without requiring either source. """ if hasattr(_marketdata_key, '_cached'): return _marketdata_key._cached key = (os.environ.get('MARKETDATA_KEY', '') or os.environ.get('MARKETDATA_API_KEY', '')) if not key: try: import subprocess out = subprocess.run( ['pass', 'show', 'env/marketdata-token'], capture_output=True, text=True, timeout=5, check=False) if out.returncode == 0: key = out.stdout.strip().splitlines()[0] if out.stdout else '' except (FileNotFoundError, subprocess.TimeoutExpired): key = '' _marketdata_key._cached = key or None return _marketdata_key._cached def _marketdata_get(path, params, api_key): """Fetch a MarketData endpoint, returning normalized row dictionaries. Raises on HTTP errors or a non-'ok' status. 'no_data' is returned as an empty list so that callers can distinguish 'nothing available' from 'request failed'. """ headers = {'Accept': 'application/json', 'Authorization': f'Bearer {api_key}'} resp = requests.get(_MD_BASE + path, params=params, headers=headers, timeout=30) resp.raise_for_status() body = resp.json() status = body.get('s') if status == 'no_data': return [] if status != 'ok': raise RuntimeError( f"MarketData {path} returned status={status!r}: " f"{body.get('errmsg') or body}") lengths = [len(v) for v in body.values() if isinstance(v, list)] n = max(lengths) if lengths else 0 rows = [] for i in range(n): row = {} for key, val in body.items(): if isinstance(val, list): row[key] = val[i] if i< len(val)= else= None= else:= row[key]=val rows.append(row)= return= rows= def= _marketdata_date(timestamp):= try:= return= datetime.utcfromtimestamp(int(timestamp)).strftime('%Y-%m-%d')= except= (TypeError,= ValueError,= OSError):= return= None= def= _occ_symbol(ticker,= option_type,= strike,= expiry):= """Build= a= standard= OCC= option= symbol= from= contract= fields."""= cp='C' if= _normalize_option_type(option_type)== 'call'= else= 'P'= exp=datetime.strptime(str(expiry), '%Y-%m-%d').strftime('%y%m%d')= strike_int=int(round(float(strike) *= 1000))= root=ticker.upper().replace('.', '')= return= f'{root}{exp}{cp}{strike_int:08d}'= Chains= are= always= fetched= with= a= broad= expiry= window= so= they= can= be= cached= and= reused= for= in-memory= selection= across= any= (delta_target,= expiry= window)= combination= in= the= sensitivity= sweep.= CHAIN_FETCH_MIN_DAYS=30 CHAIN_FETCH_MAX_DAYS=760 def= _fetch_marketdata_chain(ticker,= date_str,= option_type,= api_key,= min_days=CHAIN_FETCH_MIN_DAYS, max_days=CHAIN_FETCH_MAX_DAYS): lo,= hi=_contract_window(date_str, min_days,= max_days)= params={ 'date':= date_str,= 'from':= lo.strftime('%Y-%m-%d'),= 'to':= hi.strftime('%Y-%m-%d'),= 'side':= _normalize_option_type(option_type),= 'expiration':= 'all',= }= return= _marketdata_get(f'/options/chain/{ticker}/',= params,= api_key)= Chain= cache:= one= CSV= per= (ticker,= type,= date)= storing= the= broad-window= chain.= Lets= the= sensitivity= sweep= re-select= contracts= for= different= delta= targets= and= expiry= windows= without= refetching.= CHAIN_CACHE_DIR=os.path.join(OPTION_CACHE_DIR, 'chains')= def= _chain_cache_path(ticker,= option_type,= date_str):= option_type=_normalize_option_type(option_type) return= os.path.join(CHAIN_CACHE_DIR,= f'{ticker}-{option_type}-{date_str}.csv')= def= _load_chain_cache(ticker,= option_type,= date_str):= path=_chain_cache_path(ticker, option_type,= date_str)= if= not= os.path.exists(path):= return= None= df=pd.read_csv(path) if= df.empty:= return= []= return= df.to_dict('records')= def= _save_chain_cache(ticker,= option_type,= date_str,= chain):= if= not= chain:= return= os.makedirs(CHAIN_CACHE_DIR,= exist_ok=True) path=_chain_cache_path(ticker, option_type,= date_str)= pd.DataFrame(chain).to_csv(path,= index=False) def= _get_or_fetch_chain(ticker,= date_str,= option_type,= api_key,= fetched_counter=None): """Return= the= cached= broad= chain= for= (ticker,= type,= date),= fetching= if= absent.= Requires= ``api_key``= only= when= a= fetch= is= actually= needed.= """= chain=_load_chain_cache(ticker, option_type,= date_str)= if= chain= is= not= None:= return= chain= if= not= api_key:= raise= RuntimeError(= "MARKETDATA_KEY= is= not= set= but= a= chain= fetch= is= required= for= "= f"{ticker}= {option_type}= on= {date_str}.")= time.sleep(_MD_RATE_DELAY)= chain=_fetch_marketdata_chain(ticker, date_str,= option_type,= api_key)= if= fetched_counter= is= not= None:= fetched_counter['marketdata_chains']= +=1 _save_chain_cache(ticker,= option_type,= date_str,= chain)= return= chain= def= _fetch_marketdata_quotes(symbol,= start_date,= end_date,= api_key):= to_date=(datetime.strptime(end_date, '%Y-%m-%d')= += timedelta(days=1)).strftime('%Y-%m-%d') rows=_marketdata_get(f'/options/quotes/{symbol}/', {'from':= start_date,= 'to':= to_date},= api_key)= prices={} for= row= in= rows:= date_str=_marketdata_date(row.get('updated')) if= not= date_str:= continue= price=_parse_option_price(row) if= price= is= not= None= and= price=> 0: prices[date_str] = price return prices def _implied_vol_from_price(S, K, T, option_price, option_type): """Infer Black-Scholes volatility from an observed option mid price.""" if any(x is None for x in (S, K, T, option_price)): return None if S<= 0= or= K= <=0 or= T= <=0 or= option_price= <=0: return= None= intrinsic=max(S -= K,= 0)= if= option_type== 'call'= else= max(K= -= S,= 0)= upper=S if= option_type== 'call'= else= K= if= option_price= <= intrinsic= -= 1e-6= or= option_price=> upper * 1.5: return None lo, hi = 1e-4, 5.0 try: if (option_price< bs_price(S,= K,= T,= lo,= option_type)= -= 1e-4= or= option_price=> bs_price(S, K, T, hi, option_type) + 1e-4): return None for _ in range(80): mid = (lo + hi) / 2 if bs_price(S, K, T, mid, option_type)< option_price:= lo=mid else:= hi=mid return= (lo= += hi)= /= 2= except= (FloatingPointError,= ValueError,= ZeroDivisionError):= return= None= def= _marketdata_delta(row,= ref_date_str,= expiry,= option_type,= price):= """Use= vendor= delta= when= present;= otherwise= infer= it= from= the= quote."""= native=_safe_float(row.get('delta')) if= native= is= not= None= and= native= !=0: return= native= S=_safe_float(row.get('underlyingPrice')) K=_safe_float(row.get('strike')) ref=datetime.strptime(ref_date_str, '%Y-%m-%d')= exp=datetime.strptime(expiry, '%Y-%m-%d')= T=max((exp -= ref).days= /= 365.25,= 1e-6)= sigma=_safe_float(row.get('iv')) if= sigma= is= None= or= sigma= <=0: sigma=_implied_vol_from_price(S, K,= T,= price,= option_type)= if= S= is= None= or= K= is= None= or= sigma= is= None= or= sigma= <=0: return= None= return= bs_delta(S,= K,= T,= sigma,= option_type)= def= _select_marketdata_contract(chain,= ref_date_str,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): option_type=_normalize_option_type(option_type) lo,= hi=_contract_window(ref_date_str, min_days,= max_days)= candidates=[] for= c= in= chain:= if= str(c.get('side',= '')).lower()= !=option_type: continue= expiry=_marketdata_date(c.get('expiration')) if= not= expiry:= continue= exp=datetime.strptime(expiry, '%Y-%m-%d')= if= not= (lo= <=exp <=hi): continue= price=_parse_option_price(c) if= price= is= None= or= price= <=0: continue= delta=_marketdata_delta(c, ref_date_str,= expiry,= option_type,= price)= if= delta= is= None= or= delta== 0:= continue= strike=_safe_float(c.get('strike')) symbol=c.get('optionSymbol') if= strike= is= None= or= not= symbol:= continue= candidates.append({= 'option_type':= option_type,= 'symbol':= symbol,= 'strike':= strike,= 'expiry':= expiry,= 'delta':= delta,= 'price':= price,= })= if= not= candidates:= return= None= candidates.sort(key=lambda x:= abs(abs(x['delta'])= -= delta_target))= return= candidates[0]= def= download_option_prices(option_positions,= quarters,= holdings,= filing_dates,= today,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Download= historical= representative= option= prices= from= MarketData.= MarketData= is= the= sole= supported= provider.= MARKETDATA_KEY= must= be= set.= For= each= (ticker,= option_type)= and= each= filing= period= in= which= that= position= is= held:= 1.= On= the= first= trading= day,= select= a= contract= matching= type,= with= expiry= between= ``min_days``= and= ``max_days``= of= the= period= start,= and= |delta|= closest= to= ``delta_target``.= MarketData's= Starter= plan= often= returns= null= Greeks,= so= delta= is= inferred= from= the= observed= mid= price= via= Black-Scholes= when= the= vendor= delta= is= missing.= 2.= Lock= in= that= contract= for= the= period.= 3.= Track= its= historical= mid= price= through= the= period.= The= broad= option= chain= for= each= (ticker,= type,= first_day)= is= cached= to= disk= so= that= sensitivity= sweeps= over= (delta_target,= expiry= window)= reuse= a= single= fetch.= Raises= ``RuntimeError``= if= no= suitable= contract= can= be= selected= for= any= required= (ticker,= type,= period),= or= if= MarketData= returns= no= price= series= for= the= selected= contract.= Parameters= ----------= delta_target= := float= Target= |delta|= for= contract= selection= (default= ``OPTION_DELTA``).= min_days,= max_days= := int= Contract= expiry= window= in= days= from= period= start= (default= 270-456,= i.e.= 9-15= months).= Returns= -------= per_period= := dict= {quarter_str:= {(ticker,= type):= {date_str:= float}}}= Option= prices= keyed= by= filing= period= then= option= position.= Each= period= has= its= own= contract's= prices.= """= option_positions=sorted({ (ticker,= _normalize_option_type(pos_type))= for= ticker,= pos_type= in= option_positions})= md_key=_marketdata_key() os.makedirs(OPTION_CACHE_DIR,= exist_ok=True) per_period={} #= {q:= {(ticker,= type):= {date_str:= price}}}= fetched={'marketdata_chains': 0,= 'marketdata_quotes':= 0}= for= ticker,= option_type= in= option_positions:= opt_key=_option_position_key(ticker, option_type)= cache=_load_option_cache(ticker, option_type,= delta_target,= min_days,= max_days)= new_rows=[] for= i,= q= in= enumerate(quarters):= Skip= quarters= where= this= exact= option= position= is= absent.= if= opt_key= not= in= holdings[q]:= continue= period_start=filing_dates[q] period_end=(filing_dates[quarters[i += 1]]= if= i= <= len(quarters)= -= 1= else= today)= trading_days=pd.bdate_range(period_start, period_end)= if= len(trading_days)== 0:= continue= first_day=trading_days[0].strftime('%Y-%m-%d') --= Select= contract= on= first= trading= day= --= contract=_select_cached_contract( cache,= option_type,= first_day,= delta_target=delta_target, min_days=min_days, max_days=max_days, require_selected=True) if= contract= is= None:= chain=_get_or_fetch_chain( ticker,= first_day,= option_type,= md_key,= fetched)= contract=_select_marketdata_contract( chain,= first_day,= option_type,= delta_target=delta_target, min_days=min_days, max_days=max_days) if= contract= is= None:= raise= RuntimeError(= f"MarketData= returned= no= usable= {option_type}= contract= "= f"for= {ticker}= on= {first_day}= (period= {q})= at= "= f"delta={delta_target}, "= f"expiry= {min_days}-{max_days}d")= new_rows.append({= 'date':= first_day,= 'selected_on':= first_day,= 'option_type':= option_type,= 'symbol':= contract.get('symbol'),= 'strike':= contract['strike'],= 'expiry':= contract['expiry'],= 'delta':= contract['delta'],= 'price':= contract['price'],= })= strike=contract['strike'] expiry=contract['expiry'] symbol=contract.get('symbol') or= _occ_symbol(= ticker,= option_type,= strike,= expiry)= --= Collect= prices= for= this= period= (fresh= dict= per= period)= --= period_prices={} Fast= path:= read= matching= prices= from= cache.= rows=cache[ (cache['date']=>= period_start) & (cache['date']<= period_end)= &= (cache['option_type']== option_type)= &= (abs(cache['strike']= -= strike)= <= 0.01)= &= (cache['expiry'].astype(str)== str(expiry))= &= pd.notna(cache['price'])]= selected_rows=rows[rows['selected_on'] == first_day]= if= not= selected_rows.empty:= rows=selected_rows for= _,= row= in= rows.iterrows():= period_prices[row['date']]=float(row['price']) Decide= whether= to= refresh= quotes.= With= a= key,= refresh= whenever= the= cached= series= does= not= reach= period_end.= Without= a= key,= only= fail= if= the= cached= series= is= empty;= a= slightly= stale= tail= is= acceptable= for= cache-only= runs= (e.g.= sensitivity= sweeps= replaying= the= baseline= contract).= has_partial=bool(period_prices) reaches_end=has_partial and= max(period_prices)=>= period_end if md_key and not reaches_end: time.sleep(_MD_RATE_DELAY) quote_prices = _fetch_marketdata_quotes( symbol, period_start, period_end, md_key) fetched['marketdata_quotes'] += 1 for day_str, price in quote_prices.items(): if period_start<= day_str= <=period_end: period_prices[day_str]=price new_rows.append({= 'date':= day_str,= 'selected_on':= first_day,= 'option_type':= option_type,= 'symbol':= symbol,= 'strike':= strike,= 'expiry':= expiry,= 'delta':= contract['delta'],= 'price':= price,= })= if= contract.get('price')= and= first_day= not= in= period_prices:= period_prices[first_day]=contract['price'] elif= not= md_key= and= not= has_partial:= raise= RuntimeError(= "MARKETDATA_KEY= is= not= set= and= no= cached= quotes= exist= "= f"for= {symbol}= in= {period_start}..{period_end}.")= if= not= period_prices:= raise= RuntimeError(= f"MarketData= returned= no= quotes= for= {symbol}= "= f"({opt_key})= in= {period_start}..{period_end}")= per_period.setdefault(q,= {})[opt_key]=period_prices Persist= new= data= to= cache= if= new_rows:= new_df=pd.DataFrame(new_rows) cache=pd.concat([cache, new_df],= ignore_index=True) cache.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) cache.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) _save_option_cache(ticker,= option_type,= cache,= delta_target,= min_days,= max_days)= if= any(fetched.values()):= import= sys= parts=[] if= fetched['marketdata_chains']:= parts.append(f"{fetched['marketdata_chains']}= MarketData= chains")= if= fetched['marketdata_quotes']:= parts.append(f"{fetched['marketdata_quotes']}= MarketData= quote= series")= print(f"[options]= Fetched= {',= '.join(parts)}",= file=sys.stderr) return= per_period= --= Black-Scholes= helpers= (used= only= to= infer= delta= when= MarketData's= Starter-plan= historical= Greeks= are= null;= never= to= reprice= returns)= -----= from= scipy.stats= import= norm= as= _norm= def= bs_price(S,= K,= T,= sigma,= option_type='call' ):= """Black-Scholes= option= price= (assumes= zero= risk-free= rate= and= dividends)."""= if= T= <=0 or= sigma= <=0: if= option_type== 'call':= return= max(S= -= K,= 0)= return= max(K= -= S,= 0)= d1=(np.log(S /= K)= += (sigma= **= 2= /= 2)= *= T)= /= (sigma= *= np.sqrt(T))= d2=d1 -= sigma= *= np.sqrt(T)= if= option_type== 'call':= return= S= *= _norm.cdf(d1)= -= K= *= _norm.cdf(d2)= return= K= *= _norm.cdf(-d2)= -= S= *= _norm.cdf(-d1)= def= bs_delta(S,= K,= T,= sigma,= option_type='call' ):= """Black-Scholes= delta= (assumes= zero= risk-free= rate= and= dividends)."""= if= T= <=0 or= sigma= <=0: if= option_type== 'call':= return= 1.0= if= S=> K else 0.0 return -1.0 if S< K= else= 0.0= d1=(np.log(S /= K)= += (sigma= **= 2= /= 2)= *= T)= /= (sigma= *= np.sqrt(T))= if= option_type== 'call':= return= _norm.cdf(d1)= return= _norm.cdf(d1)= -= 1= def= daily_cumulative(holdings,= quarters,= filing_dates,= close,= today,= mode,= per_period_opt=None): """Build= a= daily= series= of= cumulative= growth= factors= for= a= given= mode.= For= each= filing= period,= stock= shares= and= option= contracts= are= fixed.= In= equity-proxy= mode,= option= rows= are= converted= to= linear= underlying= exposure:= calls= are= long= underlying= and= puts= are= short= underlying.= In= option-proxy= mode,= option= rows= are= sized= by= 13F= underlying= notional= and= returns= come= from= MarketData= quotes;= returns= are= divided= by= deployed= capital= (stock= value= plus= option= premium= cost).= Option-proxy= mode= raises= if= MarketData= prices= are= missing= for= any= required= position.= """= cum_growth=1.0 dates_out=[] values_out=[] for= i,= q= in= enumerate(quarters):= period_start=filing_dates[q] period_end=filing_dates[quarters[i += 1]]= if= i= <= len(quarters)= -= 1= else= today= ps=pd.Timestamp(period_start) pe=pd.Timestamp(period_end) Trading= days= in= this= period= mask=(close.index>= ps) & (close.index<= pe)= period_close=close[mask] if= period_close.empty:= continue= Option= prices= for= this= period= (keyed= by= (ticker,= type)= →= prices)= quarter_opt=per_period_opt.get(q, {})= if= per_period_opt= else= {}= Determine= starting= prices,= fixed= exposure,= and= deployed= capital.= positions=holdings[q] exposure={} costs={} start_prices={} start_underlying={} use_opt_px={} #= track= which= positions= use= option= prices= total_cost=0 for= (ticker,= pos_type),= value= in= positions.items():= is_option=pos_type in= ('call',= 'put')= opt_key=_option_position_key(ticker, pos_type)= if= mode== 'equity_only':= if= pos_type= not= in= ('long',= 'call',= 'put'):= continue= if= ticker= not= in= close.columns:= continue= src=close[ticker].dropna() avail=src[src.index>= ps] if avail.empty: continue stock_start = float(avail.iloc[0]) if stock_start<= 0:= continue= start_prices[(ticker,= pos_type)]=stock_start start_underlying[(ticker,= pos_type)]=stock_start costs[(ticker,= pos_type)]=value exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=False total_cost= +=value continue= mode== 'full'= (option= proxy)= if= is_option:= if= opt_key= not= in= quarter_opt:= raise= RuntimeError(= f"No= MarketData= option= prices= for= {opt_key}= in= "= f"period= {q}")= ticker_opt=quarter_opt[opt_key] opt_dates=sorted(d for= d= in= ticker_opt= if= d=>= period_start) if not opt_dates: raise RuntimeError( f"MarketData option prices for {opt_key} in period " f"{q} contain no dates at or after {period_start}") if ticker not in close.columns: raise RuntimeError( f"No underlying close series for {ticker}") src = close[ticker].dropna() avail = src[src.index >= ps] if avail.empty: raise RuntimeError( f"No underlying price for {ticker} at {period_start}") opt_start = ticker_opt[opt_dates[0]] underlying_start = float(avail.iloc[0]) if opt_start<= 0= or= underlying_start= <=0: raise= RuntimeError(= f"Non-positive= starting= price= for= {opt_key}= in= "= f"period= {q}")= start_prices[(ticker,= pos_type)]=opt_start start_underlying[(ticker,= pos_type)]=underlying_start costs[(ticker,= pos_type)]=value *= opt_start= /= underlying_start= exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=True total_cost= +=costs[(ticker, pos_type)]= continue= Plain= stock= in= full= mode= if= ticker= not= in= close.columns:= continue= src=close[ticker].dropna() avail=src[src.index>= ps] if avail.empty: continue stock_start = float(avail.iloc[0]) if stock_start<= 0:= continue= start_prices[(ticker,= pos_type)]=stock_start start_underlying[(ticker,= pos_type)]=stock_start costs[(ticker,= pos_type)]=value exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=False total_cost= +=value if= total_cost== 0:= continue= Daily= P&L= relative= to= period= start.= Skip= first= day= of= subsequent= periods= (already= recorded= as= last= day= of= the= prior= period)= to= avoid= duplicate= boundary= dates.= start_idx=1 if= i=> 0 else 0 Forward-fill: track last known option price so that gaps in option data don't cause positions to vanish mid-period. last_opt = {k: v for k, v in start_prices.items() if use_opt_px.get(k)} for day_idx in range(start_idx, len(period_close)): day = period_close.index[day_idx] day_str = day.strftime('%Y-%m-%d') period_pnl = 0 for (ticker, pos_type), value in exposure.items(): p0 = start_prices[(ticker, pos_type)] if p0 == 0: continue if use_opt_px[(ticker, pos_type)]: opt_key = _option_position_key(ticker, pos_type) p1_val = quarter_opt.get(opt_key, {}).get(day_str) if p1_val is not None: last_opt[(ticker, pos_type)] = p1_val else: p1_val = last_opt.get((ticker, pos_type)) if p1_val is None: continue underlying_p0 = start_underlying.get((ticker, pos_type)) if not underlying_p0 or underlying_p0<= 0:= continue= position_pnl=value *= (float(p1_val)= -= p0)= /= underlying_p0= else:= if= ticker= not= in= period_close.columns:= continue= p1_val=period_close[ticker].iloc[day_idx] if= pd.isna(p1_val):= continue= stock_ret=(float(p1_val) -= p0)= /= p0= if= mode== 'equity_only':= position_pnl=( value= *= _linear_underlying_sign(pos_type)= *= stock_ret)= else:= position_pnl=value *= stock_ret= period_pnl= +=position_pnl dates_out.append(day)= values_out.append(cum_growth= *= (1= += period_pnl= /= total_cost))= Chain:= next= period= starts= from= the= last= day's= growth= factor= if= values_out:= cum_growth=values_out[-1] return= dates_out,= values_out= Download= representative= option= prices= from= MarketData= (raises= on= missing= data).= option_positions=sorted({ (t,= pt)= for= q= in= quarters= for= (t,= pt)= in= holdings[q]= if= pt= in= ('call',= 'put')= })= per_period_opt=download_option_prices( option_positions,= quarters,= holdings,= filing_dates,= today)= Compute= copycat= returns= header=(f"{'Period':<16} {'Dates':<24}= "= f"{'Eq.= proxy':=>9} {'Opt. proxy':>10} {'SPY':>9}") print("COPYCAT STRATEGY RETURNS") print("=" * 72) print(header) print("-" * 72) cum_eq = 1.0 cum_full = 1.0 cum_spy = 1.0 for i, q in enumerate(quarters): start = filing_dates[q] end = filing_dates[quarters[i + 1]] if i< len(quarters)= -= 1= else= today= suffix=" †" if= i== len(quarters)= -= 1= else= ""= ret_eq=compute_return(holdings[q], prices,= start,= end,= 'equity_only')= ret_full=compute_return(holdings[q], prices,= start,= end,= 'full',= option_prices=per_period_opt.get(q, {}))= ret_spy=None if= 'SPY'= in= prices= and= start= in= prices['SPY']= and= end= in= prices['SPY']:= spy_p0,= spy_p1=prices['SPY'][start], prices['SPY'][end]= if= spy_p0= !=0: ret_spy=(spy_p1 -= spy_p0)= /= spy_p0= if= ret_eq= is= not= None:= cum_eq= *=(1 += ret_eq)= if= ret_full= is= not= None:= cum_full= *=(1 += ret_full)= if= ret_spy= is= not= None:= cum_spy= *=(1 += ret_spy)= dates_str=f"{start} to= {end}"= print(f"{q= += suffix:<16}= {dates_str:<24}= "= f"{fmt(ret_eq):=>9} {fmt(ret_full):>9} {fmt(ret_spy):>9}") print("-" * 72) cum_eq_ret = cum_eq - 1 cum_full_ret = cum_full - 1 cum_spy_ret = cum_spy - 1 dates_str = f"{first_date} to {today}" print(f"{'Cumulative':<16} {dates_str:<24}= "= f"{fmt(cum_eq_ret):=>9} {fmt(cum_full_ret):>9} {fmt(cum_spy_ret):>9}") print() print("† = partial period (still holding; updates on re-evaluation)") print("Eq. proxy = stocks plus option rows as linear underlying exposure") print("Opt. proxy = options sized to 13F notional; returns on deployed capital") ── Risk-adjusted returns ────────────────────────────────────────── daily_close = download_daily(all_tickers, first_date, today) def daily_returns_from_cumulative(mode, per_period_opt=None): if daily_close.empty: return pd.Series(dtype=float) dates, values = daily_cumulative( holdings, quarters, filing_dates, daily_close, today, mode, per_period_opt=per_period_opt) if not dates: return pd.Series(dtype=float) growth = pd.Series(values, index=dates) return growth.pct_change().dropna() ret_eq_d = daily_returns_from_cumulative('equity_only') ret_full_d = daily_returns_from_cumulative( 'full', per_period_opt=per_period_opt) if 'SPY' in daily_close.columns: spy_close = daily_close['SPY'].dropna() spy_period = spy_close[spy_close.index >= pd.Timestamp(first_date)] ret_spy_d = spy_period.pct_change().dropna() else: ret_spy_d = pd.Series(dtype=float) def sharpe(daily_rets, rf_annual=0.04): if daily_rets.empty: return float('nan') rf_daily = (1 + rf_annual) ** (1 / 252) - 1 excess = daily_rets - rf_daily if excess.std() == 0 or pd.isna(excess.std()): return float('nan') return float(excess.mean() / excess.std() * 252 ** 0.5) def max_drawdown(daily_rets): if daily_rets.empty: return float('nan') cum = (1 + daily_rets).cumprod() return float(((cum - cum.cummax()) / cum.cummax()).min() * 100) print() print("RISK-ADJUSTED RETURNS") print("=" * 55) print(f"{'Metric':<25} {'Eq.proxy':=>9} {'Opt.proxy':>9} {'SPY':>9}") print("-" * 55) vol_eq = float(ret_eq_d.std() * 252 ** 0.5 * 100) vol_full = float(ret_full_d.std() * 252 ** 0.5 * 100) vol_spy = float(ret_spy_d.std() * 252 ** 0.5 * 100) print(f"{'Ann. volatility':<25} {vol_eq:=>8.1f}% {vol_full:>8.1f}% {vol_spy:>8.1f}%") sh_eq = sharpe(ret_eq_d) sh_full = sharpe(ret_full_d) sh_spy = sharpe(ret_spy_d) print(f"{'Sharpe (rf=4%)':<25} {sh_eq:=>9.2f} {sh_full:>9.2f} {sh_spy:>9.2f}") mdd_eq = max_drawdown(ret_eq_d) mdd_full = max_drawdown(ret_full_d) mdd_spy = max_drawdown(ret_spy_d) print(f"{'Max drawdown':<25} {mdd_eq:=>8.1f}% {mdd_full:>8.1f}% {mdd_spy:>8.1f}%") ``` ```python import json import yfinance as yf import pandas as pd from datetime import datetime, timedelta import numpy as np import requests import time import os import warnings warnings.filterwarnings('ignore') Parse data from the scraper block parsed = json.loads(data) if isinstance(data, str) else data filings = parsed["filings"] Build internal structures filing_dates = {f["quarter"]: f["filing_date"] for f in filings} quarter_end_dates = {f["quarter"]: f["quarter_end"] for f in filings} quarters = [f["quarter"] for f in filings] Convert holdings list to dict keyed by quarter. Multiple positions in the same ticker with different types are aggregated by value per (ticker, type) pair. holdings = {} for f in filings: positions = {} for h in f["holdings"]: ticker = h["ticker"] pos_type = h["type"] value = h["value"] key = (ticker, pos_type) positions[key] = positions.get(key, 0) + value holdings[f["quarter"]] = positions def _extract_close_series(df, ticker): """Extract a single close-price series from a yfinance result.""" if df.empty: return pd.Series(dtype=float) if isinstance(df.columns, pd.MultiIndex): if 'Close' not in df.columns.get_level_values(0): return pd.Series(dtype=float) close = df['Close'] if isinstance(close, pd.DataFrame): if ticker in close.columns: series = close[ticker] elif len(close.columns) == 1: series = close.iloc[:, 0] else: return pd.Series(dtype=float) else: series = close elif 'Close' in df.columns: series = df['Close'] if isinstance(series, pd.DataFrame): series = series.iloc[:, 0] else: return pd.Series(dtype=float) return pd.to_numeric(series, errors='coerce').dropna() def _download_close_series(ticker, start, end): """Download one ticker's close series; used to repair flaky batch misses.""" df = yf.download(ticker, start=start, end=end, progress=False, auto_adjust=True) return _extract_close_series(df, ticker) def get_prices(tickers, dates): """Fetch close prices for tickers on specific dates.""" unique_tickers = sorted(set(tickers)) all_dates = [datetime.strptime(d, '%Y-%m-%d') for d in dates] start = min(all_dates) - timedelta(days=5) end = max(all_dates) + timedelta(days=5) df = yf.download(unique_tickers, start=start, end=end, progress=False, auto_adjust=True) yf.download returns MultiIndex columns (metric, ticker) for multiple tickers if df.empty: close = pd.DataFrame() elif isinstance(df.columns, pd.MultiIndex) and 'Close' in df.columns.get_level_values(0): close = df['Close'].copy() elif 'Close' in df.columns: close = df[['Close']].copy() close.columns = unique_tickers else: close = pd.DataFrame() prices = {} for ticker in unique_tickers: if ticker in close.columns: series = pd.to_numeric(close[ticker], errors='coerce').dropna() else: series = pd.Series(dtype=float) if series.empty: series = _download_close_series(ticker, start, end) if series.empty: continue prices[ticker] = {} for date_str in dates: target = pd.Timestamp(datetime.strptime(date_str, '%Y-%m-%d')) after = series[series.index >= target] if not after.empty: prices[ticker][date_str] = float(after.iloc[0]) else: before = series[series.index<= target]= if= not= before.empty:= prices[ticker][date_str]=float(before.iloc[-1]) return= prices= def= _price_on_or_after(px_by_date,= target_date):= """Return= (date,= price)= for= the= first= available= price= on/after= target."""= if= not= px_by_date:= return= None= dates=sorted(d for= d= in= px_by_date= if= d=>= target_date) if not dates: return None d = dates[0] return d, px_by_date[d] def _price_on_or_before(px_by_date, target_date): """Return (date, price) for the last available price on/before target.""" if not px_by_date: return None dates = sorted(d for d in px_by_date if d<= target_date)= if= not= dates:= return= None= d=dates[-1] return= d,= px_by_date[d]= def= _period_price_pair(px_by_date,= start_date,= end_date):= """Return= start/end= prices= for= a= period= using= sensible= boundary= alignment."""= start=_price_on_or_after(px_by_date, start_date)= end=_price_on_or_before(px_by_date, end_date)= if= start= is= None= or= end= is= None:= return= None= start_actual,= p0=start end_actual,= p1=end if= end_actual= <= start_actual:= return= None= return= start_actual,= end_actual,= p0,= p1= def= _option_position_key(ticker,= pos_type):= return= (ticker,= pos_type)= def= _linear_underlying_sign(pos_type):= """Direction= when= option= rows= are= converted= to= underlying= equity= exposure."""= return= -1= if= pos_type== 'put'= else= 1= def= compute_return(positions,= prices,= start_date,= end_date,= mode='equity_only' ,= option_prices=None): """Compute= portfolio= return= between= two= dates.= The= 13F= value= for= an= option= row= is= treated= as= underlying= notional,= not= option= premium.= Option= contracts= are= sized= from= that= notional,= but= the= portfolio= denominator= is= estimated= deployed= capital:= stock= value= plus= option= premium= cost.= This= avoids= treating= the= gap= between= option= notional= and= option= premium= as= cash.= In= 'full'= mode,= every= option= row= requires= a= MarketData= price= series;= missing= data= raises= rather= than= falling= back.= """= total_cost=0 portfolio_pnl=0 for= (ticker,= pos_type),= value= in= positions.items():= is_option=pos_type in= ('call',= 'put')= stock_px=prices.get(ticker) if= mode== 'equity_only':= if= pos_type= not= in= ('long',= 'call',= 'put'):= continue= pair=_period_price_pair(stock_px, start_date,= end_date)= if= pair= is= None:= continue= start_actual,= end_actual,= p0,= p1=pair if= p0== 0:= continue= stock_ret=(p1 -= p0)= /= p0= total_cost= +=value portfolio_pnl= +=value *= _linear_underlying_sign(pos_type)= *= stock_ret= continue= if= is_option:= opt_key=_option_position_key(ticker, pos_type)= opt_px=option_prices.get(opt_key) if= option_prices= else= None= if= not= opt_px:= raise= RuntimeError(= f"No= MarketData= option= prices= for= {opt_key}= in= period= "= f"{start_date}..{end_date}")= pair=_period_price_pair(opt_px, start_date,= end_date)= if= pair= is= None:= raise= RuntimeError(= f"MarketData= option= price= series= for= {opt_key}= does= not= "= f"cover= {start_date}..{end_date}")= start_actual,= end_actual,= opt_p0,= opt_p1=pair stock_start=_price_on_or_after(stock_px, start_actual)= if= stock_start= is= None= or= stock_start[1]= <=0: stock_start=_price_on_or_after(stock_px, start_date)= if= stock_start= is= None= or= stock_start[1]= <=0: raise= RuntimeError(= f"No= underlying= price= for= {ticker}= at= {start_date}")= p0,= p1=opt_p0, opt_p1= underlying_p0=stock_start[1] if= p0= <=0 or= underlying_p0= <=0: continue= position_cost=value *= (p0= /= underlying_p0)= position_pnl=value *= ((p1= -= p0)= /= underlying_p0)= else:= pair=_period_price_pair(stock_px, start_date,= end_date)= if= pair= is= None:= continue= start_actual,= end_actual,= p0,= p1=pair if= p0== 0:= continue= stock_ret=(p1 -= p0)= /= p0= position_cost=value position_pnl=value *= stock_ret= if= position_cost= <=0: continue= total_cost= +=position_cost portfolio_pnl= +=position_pnl return= portfolio_pnl= /= total_cost= if= total_cost= else= None= def= annualize(ret,= days):= """Annualize= a= return= over= a= given= number= of= calendar= days."""= if= ret= is= None= or= days= <=0: return= None= return= (1= += ret)= **= (365.25= /= days)= -= 1= def= fmt(ret):= return= f"{ret= *= 100:+.2f}%"= if= ret= is= not= None= else= "N/A"= Collect= all= tickers= and= dates= all_tickers=set() for= positions= in= holdings.values():= for= (ticker,= _)= in= positions:= all_tickers.add(ticker)= all_tickers.add('SPY')= today=datetime.now().strftime('%Y-%m-%d') first_date=filing_dates[quarters[0]] all_dates=set(filing_dates.values()) |= set(quarter_end_dates.values())= |= {today}= prices=get_prices(sorted(all_tickers), sorted(all_dates))= Resolve= `today`= to= the= actual= last= available= closing= date.= yfinance= may= not= have= data= for= today= (market= still= open= or= holiday),= so= we= look= up= what= date= SPY's= price= actually= corresponds= to.= def= _resolve_price_date(prices,= requested_date):= """Return= the= actual= trading= date= of= the= price= stored= under= requested_date."""= ref='SPY' if= 'SPY'= in= prices= else= next(iter(prices),= None)= if= not= ref= or= requested_date= not= in= prices[ref]:= return= requested_date= target_price=prices[ref][requested_date] Re-download= a= small= window= to= find= the= real= date= of= this= price= start=datetime.strptime(requested_date, '%Y-%m-%d')= -= timedelta(days=10) end=datetime.strptime(requested_date, '%Y-%m-%d')= += timedelta(days=5) df=yf.download(ref, start=start, end=end, progress=False, auto_adjust=True) if= df.empty:= return= requested_date= if= isinstance(df.columns,= pd.MultiIndex):= close=df['Close'][ref].dropna() elif= 'Close'= in= df.columns:= close=df['Close'].dropna() else:= close=df.iloc[:, 0].dropna()= for= dt,= px= in= close.items():= val=float(px.iloc[0]) if= isinstance(px,= pd.Series)= else= float(px)= if= abs(val= -= target_price)= <= 0.01:= ts=dt[0] if= isinstance(dt,= tuple)= else= dt= return= pd.Timestamp(ts).strftime('%Y-%m-%d')= return= requested_date= today_resolved=_resolve_price_date(prices, today)= if= today_resolved= !=today: for= ticker= in= prices:= if= today= in= prices[ticker]:= prices[ticker][today_resolved]=prices[ticker].pop(today) today=today_resolved def= download_daily(tickers,= start_date,= end_date):= """Download= daily= close= prices= from= yfinance,= handling= MultiIndex.= Dates= are= 'YYYY-MM-DD'= strings.= Adds= a= small= buffer= for= trading-day= alignment."""= tickers_sorted=sorted(tickers) start=datetime.strptime(start_date, '%Y-%m-%d')= -= timedelta(days=5) end=datetime.strptime(end_date, '%Y-%m-%d')= += timedelta(days=5) df=yf.download(tickers_sorted, start=start, end=end, progress=False, auto_adjust=True) if= df.empty:= close=pd.DataFrame() elif= isinstance(df.columns,= pd.MultiIndex)= and= 'Close'= in= df.columns.get_level_values(0):= close=df['Close'].copy() elif= 'Close'= in= df.columns:= close=df[['Close']].copy() close.columns=tickers_sorted else:= close=pd.DataFrame() for= ticker= in= tickers_sorted:= if= ticker= in= close.columns= and= not= close[ticker].dropna().empty:= continue= series=_download_close_series(ticker, start,= end)= if= not= series.empty:= close[ticker]=series return= close.sort_index()= --= Historical= option= prices= via= MarketData= --------------------------------= OPTION_CACHE_DIR=os.path.expanduser('~/My Drive/notes/.sa-lp-option-cache')= _MD_BASE='https://api.marketdata.app/v1' _MD_RATE_DELAY=0.15 OPTION_CACHE_COLUMNS=[ 'date',= 'selected_on',= 'option_type',= 'symbol',= 'strike',= 'expiry',= 'delta',= 'price']= Default= contract= selection= parameters.= The= option= proxy= picks= a= contract= matching= option= type,= with= expiry= between= min_days= and= max_days= of= the= period= start,= and= |delta|= closest= to= delta_target.= When= the= chain= is= sparse,= the= achieved= |delta|= may= be= far= from= the= target;= the= sensitivity= block= reports= achieved= |delta|= so= this= is= visible= rather= than= silent.= OPTION_DELTA=0.15 EXPIRY_MIN_DAYS=270 #= ~9= months= EXPIRY_MAX_DAYS=456 #= ~15= months= def= _normalize_option_type(option_type):= option_type=str(option_type).lower() if= option_type= not= in= ('call',= 'put'):= raise= ValueError(f"Unsupported= option= type:= {option_type}")= return= option_type= def= _empty_option_cache():= return= pd.DataFrame(columns=OPTION_CACHE_COLUMNS) def= _option_cache_path(ticker,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Return= the= cache= CSV= path= for= (ticker,= type,= delta_target,= window).= When= the= parameter= triple= equals= the= baseline= (0.15,= 270-456= days),= the= historical= filename= ``TICKER-TYPE.csv``= is= used= so= the= main-backtest= cache= is= reused= automatically.= Any= non-baseline= combo= lives= in= a= separate= ``TICKER-TYPE-d-e-.csv`` file so a sensitivity sweep never pollutes the baseline cache (which the portfolio calculator reads to pick the representative contract for the current filing). """ option_type = _normalize_option_type(option_type) is_baseline = ( abs(delta_target - OPTION_DELTA)< 1e-9= and= min_days== EXPIRY_MIN_DAYS= and= max_days== EXPIRY_MAX_DAYS)= if= is_baseline:= return= os.path.join(OPTION_CACHE_DIR,= f'{ticker}-{option_type}.csv')= return= os.path.join(= OPTION_CACHE_DIR,= f'{ticker}-{option_type}-d{delta_target:g}-e{min_days}-{max_days}.csv')= def= _load_option_cache(ticker,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Load= cached= MarketData= rows= for= a= ticker/type/target/window.= Returns= DataFrame= or= empty."""= option_type=_normalize_option_type(option_type) path=_option_cache_path(ticker, option_type,= delta_target,= min_days,= max_days)= if= not= os.path.exists(path):= return= _empty_option_cache()= df=pd.read_csv(path) if= df.empty:= return= _empty_option_cache()= for= col= in= OPTION_CACHE_COLUMNS:= if= col= not= in= df.columns:= df[col]=np.nan for= col= in= ('date',= 'selected_on'):= df[col]=pd.to_datetime( df[col],= errors='coerce' ).dt.strftime('%Y-%m-%d')= df['option_type']=df['option_type'].fillna(option_type).str.lower() cache=df[OPTION_CACHE_COLUMNS].copy() cache=cache[cache['option_type'] == option_type].copy()= cache.dropna(subset=['date'], inplace=True) for= col= in= ('strike',= 'delta',= 'price'):= cache[col]=pd.to_numeric(cache[col], errors='coerce' )= cache.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) cache.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) return= cache[OPTION_CACHE_COLUMNS]= def= _save_option_cache(ticker,= option_type,= df,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Persist= typed= option= cache= to= CSV."""= option_type=_normalize_option_type(option_type) os.makedirs(OPTION_CACHE_DIR,= exist_ok=True) path=_option_cache_path(ticker, option_type,= delta_target,= min_days,= max_days)= if= df.empty:= df=_empty_option_cache() else:= df=df.copy() df['option_type']=option_type for= col= in= OPTION_CACHE_COLUMNS:= if= col= not= in= df.columns:= df[col]=np.nan df.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) df.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) df.to_csv(path,= index=False) def= _contract_window(ref_date_str,= min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): ref=datetime.strptime(ref_date_str, '%Y-%m-%d')= return= ref= += timedelta(days=min_days), ref= += timedelta(days=max_days) def= _contract_from_cache_row(row,= ref_date_str,= option_type,= min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): option_type=_normalize_option_type(option_type) if= str(row.get('option_type',= option_type)).lower()= !=option_type: return= None= lo,= hi=_contract_window(ref_date_str, min_days,= max_days)= try:= exp=datetime.strptime(str(row['expiry']), '%Y-%m-%d')= except= (KeyError,= TypeError,= ValueError):= return= None= if= not= (lo= <=exp <=hi): return= None= strike=_safe_float(row.get('strike')) delta=_safe_float(row.get('delta')) price=_safe_float(row.get('price')) if= strike= is= None= or= delta= is= None= or= price= is= None= or= price= <=0: return= None= return= {= 'selected_on':= row.get('selected_on'),= 'option_type':= option_type,= 'symbol':= row.get('symbol'),= 'strike':= strike,= 'expiry':= str(row['expiry']),= 'delta':= delta,= 'price':= price,= }= def= _select_cached_contract(cache,= option_type,= ref_date_str,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS, require_selected=False): rows=cache[(cache['date'] == ref_date_str)= &= (cache['option_type']== option_type)]= selected_rows=rows[rows['selected_on'] == ref_date_str]= if= not= selected_rows.empty:= rows=selected_rows elif= require_selected:= rows=selected_rows candidates=[] for= _,= row= in= rows.iterrows():= contract=_contract_from_cache_row(row, ref_date_str,= option_type,= min_days,= max_days)= if= contract:= candidates.append(contract)= if= not= candidates:= return= None= candidates.sort(key=lambda x:= abs(abs(x['delta'])= -= delta_target))= return= candidates[0]= def= _parse_option_price(contract):= """Extract= a= mark= price= from= an= option= contract= record."""= mid=_safe_float(contract.get('mid')) if= mid= and= mid=> 0: return mid bid = _safe_float(contract.get('bid')) ask = _safe_float(contract.get('ask')) last = _safe_float(contract.get('last')) if bid and ask and bid > 0 and ask > 0: return (bid + ask) / 2 if last and last > 0: return last return None def _safe_float(val): try: out = float(val) if np.isnan(out): return None return out except (TypeError, ValueError): return None def _marketdata_key(): """Return the MarketData API key, or None if unavailable. Resolution order: 1. ``MARKETDATA_KEY`` / ``MARKETDATA_API_KEY`` environment variables. 2. ``pass env/marketdata-token`` (local ``pass`` store). The result is memoised on the function object so repeated lookups during a sweep do not reshell. Fetch helpers raise themselves when called without a key, so a fully cached run still succeeds without requiring either source. """ if hasattr(_marketdata_key, '_cached'): return _marketdata_key._cached key = (os.environ.get('MARKETDATA_KEY', '') or os.environ.get('MARKETDATA_API_KEY', '')) if not key: try: import subprocess out = subprocess.run( ['pass', 'show', 'env/marketdata-token'], capture_output=True, text=True, timeout=5, check=False) if out.returncode == 0: key = out.stdout.strip().splitlines()[0] if out.stdout else '' except (FileNotFoundError, subprocess.TimeoutExpired): key = '' _marketdata_key._cached = key or None return _marketdata_key._cached def _marketdata_get(path, params, api_key): """Fetch a MarketData endpoint, returning normalized row dictionaries. Raises on HTTP errors or a non-'ok' status. 'no_data' is returned as an empty list so that callers can distinguish 'nothing available' from 'request failed'. """ headers = {'Accept': 'application/json', 'Authorization': f'Bearer {api_key}'} resp = requests.get(_MD_BASE + path, params=params, headers=headers, timeout=30) resp.raise_for_status() body = resp.json() status = body.get('s') if status == 'no_data': return [] if status != 'ok': raise RuntimeError( f"MarketData {path} returned status={status!r}: " f"{body.get('errmsg') or body}") lengths = [len(v) for v in body.values() if isinstance(v, list)] n = max(lengths) if lengths else 0 rows = [] for i in range(n): row = {} for key, val in body.items(): if isinstance(val, list): row[key] = val[i] if i< len(val)= else= None= else:= row[key]=val rows.append(row)= return= rows= def= _marketdata_date(timestamp):= try:= return= datetime.utcfromtimestamp(int(timestamp)).strftime('%Y-%m-%d')= except= (TypeError,= ValueError,= OSError):= return= None= def= _occ_symbol(ticker,= option_type,= strike,= expiry):= """Build= a= standard= OCC= option= symbol= from= contract= fields."""= cp='C' if= _normalize_option_type(option_type)== 'call'= else= 'P'= exp=datetime.strptime(str(expiry), '%Y-%m-%d').strftime('%y%m%d')= strike_int=int(round(float(strike) *= 1000))= root=ticker.upper().replace('.', '')= return= f'{root}{exp}{cp}{strike_int:08d}'= Chains= are= always= fetched= with= a= broad= expiry= window= so= they= can= be= cached= and= reused= for= in-memory= selection= across= any= (delta_target,= expiry= window)= combination= in= the= sensitivity= sweep.= CHAIN_FETCH_MIN_DAYS=30 CHAIN_FETCH_MAX_DAYS=760 def= _fetch_marketdata_chain(ticker,= date_str,= option_type,= api_key,= min_days=CHAIN_FETCH_MIN_DAYS, max_days=CHAIN_FETCH_MAX_DAYS): lo,= hi=_contract_window(date_str, min_days,= max_days)= params={ 'date':= date_str,= 'from':= lo.strftime('%Y-%m-%d'),= 'to':= hi.strftime('%Y-%m-%d'),= 'side':= _normalize_option_type(option_type),= 'expiration':= 'all',= }= return= _marketdata_get(f'/options/chain/{ticker}/',= params,= api_key)= Chain= cache:= one= CSV= per= (ticker,= type,= date)= storing= the= broad-window= chain.= Lets= the= sensitivity= sweep= re-select= contracts= for= different= delta= targets= and= expiry= windows= without= refetching.= CHAIN_CACHE_DIR=os.path.join(OPTION_CACHE_DIR, 'chains')= def= _chain_cache_path(ticker,= option_type,= date_str):= option_type=_normalize_option_type(option_type) return= os.path.join(CHAIN_CACHE_DIR,= f'{ticker}-{option_type}-{date_str}.csv')= def= _load_chain_cache(ticker,= option_type,= date_str):= path=_chain_cache_path(ticker, option_type,= date_str)= if= not= os.path.exists(path):= return= None= df=pd.read_csv(path) if= df.empty:= return= []= return= df.to_dict('records')= def= _save_chain_cache(ticker,= option_type,= date_str,= chain):= if= not= chain:= return= os.makedirs(CHAIN_CACHE_DIR,= exist_ok=True) path=_chain_cache_path(ticker, option_type,= date_str)= pd.DataFrame(chain).to_csv(path,= index=False) def= _get_or_fetch_chain(ticker,= date_str,= option_type,= api_key,= fetched_counter=None): """Return= the= cached= broad= chain= for= (ticker,= type,= date),= fetching= if= absent.= Requires= ``api_key``= only= when= a= fetch= is= actually= needed.= """= chain=_load_chain_cache(ticker, option_type,= date_str)= if= chain= is= not= None:= return= chain= if= not= api_key:= raise= RuntimeError(= "MARKETDATA_KEY= is= not= set= but= a= chain= fetch= is= required= for= "= f"{ticker}= {option_type}= on= {date_str}.")= time.sleep(_MD_RATE_DELAY)= chain=_fetch_marketdata_chain(ticker, date_str,= option_type,= api_key)= if= fetched_counter= is= not= None:= fetched_counter['marketdata_chains']= +=1 _save_chain_cache(ticker,= option_type,= date_str,= chain)= return= chain= def= _fetch_marketdata_quotes(symbol,= start_date,= end_date,= api_key):= to_date=(datetime.strptime(end_date, '%Y-%m-%d')= += timedelta(days=1)).strftime('%Y-%m-%d') rows=_marketdata_get(f'/options/quotes/{symbol}/', {'from':= start_date,= 'to':= to_date},= api_key)= prices={} for= row= in= rows:= date_str=_marketdata_date(row.get('updated')) if= not= date_str:= continue= price=_parse_option_price(row) if= price= is= not= None= and= price=> 0: prices[date_str] = price return prices def _implied_vol_from_price(S, K, T, option_price, option_type): """Infer Black-Scholes volatility from an observed option mid price.""" if any(x is None for x in (S, K, T, option_price)): return None if S<= 0= or= K= <=0 or= T= <=0 or= option_price= <=0: return= None= intrinsic=max(S -= K,= 0)= if= option_type== 'call'= else= max(K= -= S,= 0)= upper=S if= option_type== 'call'= else= K= if= option_price= <= intrinsic= -= 1e-6= or= option_price=> upper * 1.5: return None lo, hi = 1e-4, 5.0 try: if (option_price< bs_price(S,= K,= T,= lo,= option_type)= -= 1e-4= or= option_price=> bs_price(S, K, T, hi, option_type) + 1e-4): return None for _ in range(80): mid = (lo + hi) / 2 if bs_price(S, K, T, mid, option_type)< option_price:= lo=mid else:= hi=mid return= (lo= += hi)= /= 2= except= (FloatingPointError,= ValueError,= ZeroDivisionError):= return= None= def= _marketdata_delta(row,= ref_date_str,= expiry,= option_type,= price):= """Use= vendor= delta= when= present;= otherwise= infer= it= from= the= quote."""= native=_safe_float(row.get('delta')) if= native= is= not= None= and= native= !=0: return= native= S=_safe_float(row.get('underlyingPrice')) K=_safe_float(row.get('strike')) ref=datetime.strptime(ref_date_str, '%Y-%m-%d')= exp=datetime.strptime(expiry, '%Y-%m-%d')= T=max((exp -= ref).days= /= 365.25,= 1e-6)= sigma=_safe_float(row.get('iv')) if= sigma= is= None= or= sigma= <=0: sigma=_implied_vol_from_price(S, K,= T,= price,= option_type)= if= S= is= None= or= K= is= None= or= sigma= is= None= or= sigma= <=0: return= None= return= bs_delta(S,= K,= T,= sigma,= option_type)= def= _select_marketdata_contract(chain,= ref_date_str,= option_type,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): option_type=_normalize_option_type(option_type) lo,= hi=_contract_window(ref_date_str, min_days,= max_days)= candidates=[] for= c= in= chain:= if= str(c.get('side',= '')).lower()= !=option_type: continue= expiry=_marketdata_date(c.get('expiration')) if= not= expiry:= continue= exp=datetime.strptime(expiry, '%Y-%m-%d')= if= not= (lo= <=exp <=hi): continue= price=_parse_option_price(c) if= price= is= None= or= price= <=0: continue= delta=_marketdata_delta(c, ref_date_str,= expiry,= option_type,= price)= if= delta= is= None= or= delta== 0:= continue= strike=_safe_float(c.get('strike')) symbol=c.get('optionSymbol') if= strike= is= None= or= not= symbol:= continue= candidates.append({= 'option_type':= option_type,= 'symbol':= symbol,= 'strike':= strike,= 'expiry':= expiry,= 'delta':= delta,= 'price':= price,= })= if= not= candidates:= return= None= candidates.sort(key=lambda x:= abs(abs(x['delta'])= -= delta_target))= return= candidates[0]= def= download_option_prices(option_positions,= quarters,= holdings,= filing_dates,= today,= delta_target=OPTION_DELTA, min_days=EXPIRY_MIN_DAYS, max_days=EXPIRY_MAX_DAYS): """Download= historical= representative= option= prices= from= MarketData.= MarketData= is= the= sole= supported= provider.= MARKETDATA_KEY= must= be= set.= For= each= (ticker,= option_type)= and= each= filing= period= in= which= that= position= is= held:= 1.= On= the= first= trading= day,= select= a= contract= matching= type,= with= expiry= between= ``min_days``= and= ``max_days``= of= the= period= start,= and= |delta|= closest= to= ``delta_target``.= MarketData's= Starter= plan= often= returns= null= Greeks,= so= delta= is= inferred= from= the= observed= mid= price= via= Black-Scholes= when= the= vendor= delta= is= missing.= 2.= Lock= in= that= contract= for= the= period.= 3.= Track= its= historical= mid= price= through= the= period.= The= broad= option= chain= for= each= (ticker,= type,= first_day)= is= cached= to= disk= so= that= sensitivity= sweeps= over= (delta_target,= expiry= window)= reuse= a= single= fetch.= Raises= ``RuntimeError``= if= no= suitable= contract= can= be= selected= for= any= required= (ticker,= type,= period),= or= if= MarketData= returns= no= price= series= for= the= selected= contract.= Parameters= ----------= delta_target= := float= Target= |delta|= for= contract= selection= (default= ``OPTION_DELTA``).= min_days,= max_days= := int= Contract= expiry= window= in= days= from= period= start= (default= 270-456,= i.e.= 9-15= months).= Returns= -------= per_period= := dict= {quarter_str:= {(ticker,= type):= {date_str:= float}}}= Option= prices= keyed= by= filing= period= then= option= position.= Each= period= has= its= own= contract's= prices.= """= option_positions=sorted({ (ticker,= _normalize_option_type(pos_type))= for= ticker,= pos_type= in= option_positions})= md_key=_marketdata_key() os.makedirs(OPTION_CACHE_DIR,= exist_ok=True) per_period={} #= {q:= {(ticker,= type):= {date_str:= price}}}= fetched={'marketdata_chains': 0,= 'marketdata_quotes':= 0}= for= ticker,= option_type= in= option_positions:= opt_key=_option_position_key(ticker, option_type)= cache=_load_option_cache(ticker, option_type,= delta_target,= min_days,= max_days)= new_rows=[] for= i,= q= in= enumerate(quarters):= Skip= quarters= where= this= exact= option= position= is= absent.= if= opt_key= not= in= holdings[q]:= continue= period_start=filing_dates[q] period_end=(filing_dates[quarters[i += 1]]= if= i= <= len(quarters)= -= 1= else= today)= trading_days=pd.bdate_range(period_start, period_end)= if= len(trading_days)== 0:= continue= first_day=trading_days[0].strftime('%Y-%m-%d') --= Select= contract= on= first= trading= day= --= contract=_select_cached_contract( cache,= option_type,= first_day,= delta_target=delta_target, min_days=min_days, max_days=max_days, require_selected=True) if= contract= is= None:= chain=_get_or_fetch_chain( ticker,= first_day,= option_type,= md_key,= fetched)= contract=_select_marketdata_contract( chain,= first_day,= option_type,= delta_target=delta_target, min_days=min_days, max_days=max_days) if= contract= is= None:= raise= RuntimeError(= f"MarketData= returned= no= usable= {option_type}= contract= "= f"for= {ticker}= on= {first_day}= (period= {q})= at= "= f"delta={delta_target}, "= f"expiry= {min_days}-{max_days}d")= new_rows.append({= 'date':= first_day,= 'selected_on':= first_day,= 'option_type':= option_type,= 'symbol':= contract.get('symbol'),= 'strike':= contract['strike'],= 'expiry':= contract['expiry'],= 'delta':= contract['delta'],= 'price':= contract['price'],= })= strike=contract['strike'] expiry=contract['expiry'] symbol=contract.get('symbol') or= _occ_symbol(= ticker,= option_type,= strike,= expiry)= --= Collect= prices= for= this= period= (fresh= dict= per= period)= --= period_prices={} Fast= path:= read= matching= prices= from= cache.= rows=cache[ (cache['date']=>= period_start) & (cache['date']<= period_end)= &= (cache['option_type']== option_type)= &= (abs(cache['strike']= -= strike)= <= 0.01)= &= (cache['expiry'].astype(str)== str(expiry))= &= pd.notna(cache['price'])]= selected_rows=rows[rows['selected_on'] == first_day]= if= not= selected_rows.empty:= rows=selected_rows for= _,= row= in= rows.iterrows():= period_prices[row['date']]=float(row['price']) Decide= whether= to= refresh= quotes.= With= a= key,= refresh= whenever= the= cached= series= does= not= reach= period_end.= Without= a= key,= only= fail= if= the= cached= series= is= empty;= a= slightly= stale= tail= is= acceptable= for= cache-only= runs= (e.g.= sensitivity= sweeps= replaying= the= baseline= contract).= has_partial=bool(period_prices) reaches_end=has_partial and= max(period_prices)=>= period_end if md_key and not reaches_end: time.sleep(_MD_RATE_DELAY) quote_prices = _fetch_marketdata_quotes( symbol, period_start, period_end, md_key) fetched['marketdata_quotes'] += 1 for day_str, price in quote_prices.items(): if period_start<= day_str= <=period_end: period_prices[day_str]=price new_rows.append({= 'date':= day_str,= 'selected_on':= first_day,= 'option_type':= option_type,= 'symbol':= symbol,= 'strike':= strike,= 'expiry':= expiry,= 'delta':= contract['delta'],= 'price':= price,= })= if= contract.get('price')= and= first_day= not= in= period_prices:= period_prices[first_day]=contract['price'] elif= not= md_key= and= not= has_partial:= raise= RuntimeError(= "MARKETDATA_KEY= is= not= set= and= no= cached= quotes= exist= "= f"for= {symbol}= in= {period_start}..{period_end}.")= if= not= period_prices:= raise= RuntimeError(= f"MarketData= returned= no= quotes= for= {symbol}= "= f"({opt_key})= in= {period_start}..{period_end}")= per_period.setdefault(q,= {})[opt_key]=period_prices Persist= new= data= to= cache= if= new_rows:= new_df=pd.DataFrame(new_rows) cache=pd.concat([cache, new_df],= ignore_index=True) cache.drop_duplicates(= subset=['date', 'selected_on',= 'option_type',= 'strike',= 'expiry'],= keep='last' ,= inplace=True) cache.sort_values(['date',= 'expiry',= 'strike'],= inplace=True) _save_option_cache(ticker,= option_type,= cache,= delta_target,= min_days,= max_days)= if= any(fetched.values()):= import= sys= parts=[] if= fetched['marketdata_chains']:= parts.append(f"{fetched['marketdata_chains']}= MarketData= chains")= if= fetched['marketdata_quotes']:= parts.append(f"{fetched['marketdata_quotes']}= MarketData= quote= series")= print(f"[options]= Fetched= {',= '.join(parts)}",= file=sys.stderr) return= per_period= --= Black-Scholes= helpers= (used= only= to= infer= delta= when= MarketData's= Starter-plan= historical= Greeks= are= null;= never= to= reprice= returns)= -----= from= scipy.stats= import= norm= as= _norm= def= bs_price(S,= K,= T,= sigma,= option_type='call' ):= """Black-Scholes= option= price= (assumes= zero= risk-free= rate= and= dividends)."""= if= T= <=0 or= sigma= <=0: if= option_type== 'call':= return= max(S= -= K,= 0)= return= max(K= -= S,= 0)= d1=(np.log(S /= K)= += (sigma= **= 2= /= 2)= *= T)= /= (sigma= *= np.sqrt(T))= d2=d1 -= sigma= *= np.sqrt(T)= if= option_type== 'call':= return= S= *= _norm.cdf(d1)= -= K= *= _norm.cdf(d2)= return= K= *= _norm.cdf(-d2)= -= S= *= _norm.cdf(-d1)= def= bs_delta(S,= K,= T,= sigma,= option_type='call' ):= """Black-Scholes= delta= (assumes= zero= risk-free= rate= and= dividends)."""= if= T= <=0 or= sigma= <=0: if= option_type== 'call':= return= 1.0= if= S=> K else 0.0 return -1.0 if S< K= else= 0.0= d1=(np.log(S /= K)= += (sigma= **= 2= /= 2)= *= T)= /= (sigma= *= np.sqrt(T))= if= option_type== 'call':= return= _norm.cdf(d1)= return= _norm.cdf(d1)= -= 1= def= daily_cumulative(holdings,= quarters,= filing_dates,= close,= today,= mode,= per_period_opt=None): """Build= a= daily= series= of= cumulative= growth= factors= for= a= given= mode.= For= each= filing= period,= stock= shares= and= option= contracts= are= fixed.= In= equity-proxy= mode,= option= rows= are= converted= to= linear= underlying= exposure:= calls= are= long= underlying= and= puts= are= short= underlying.= In= option-proxy= mode,= option= rows= are= sized= by= 13F= underlying= notional= and= returns= come= from= MarketData= quotes;= returns= are= divided= by= deployed= capital= (stock= value= plus= option= premium= cost).= Option-proxy= mode= raises= if= MarketData= prices= are= missing= for= any= required= position.= """= cum_growth=1.0 dates_out=[] values_out=[] for= i,= q= in= enumerate(quarters):= period_start=filing_dates[q] period_end=filing_dates[quarters[i += 1]]= if= i= <= len(quarters)= -= 1= else= today= ps=pd.Timestamp(period_start) pe=pd.Timestamp(period_end) Trading= days= in= this= period= mask=(close.index>= ps) & (close.index<= pe)= period_close=close[mask] if= period_close.empty:= continue= Option= prices= for= this= period= (keyed= by= (ticker,= type)= →= prices)= quarter_opt=per_period_opt.get(q, {})= if= per_period_opt= else= {}= Determine= starting= prices,= fixed= exposure,= and= deployed= capital.= positions=holdings[q] exposure={} costs={} start_prices={} start_underlying={} use_opt_px={} #= track= which= positions= use= option= prices= total_cost=0 for= (ticker,= pos_type),= value= in= positions.items():= is_option=pos_type in= ('call',= 'put')= opt_key=_option_position_key(ticker, pos_type)= if= mode== 'equity_only':= if= pos_type= not= in= ('long',= 'call',= 'put'):= continue= if= ticker= not= in= close.columns:= continue= src=close[ticker].dropna() avail=src[src.index>= ps] if avail.empty: continue stock_start = float(avail.iloc[0]) if stock_start<= 0:= continue= start_prices[(ticker,= pos_type)]=stock_start start_underlying[(ticker,= pos_type)]=stock_start costs[(ticker,= pos_type)]=value exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=False total_cost= +=value continue= mode== 'full'= (option= proxy)= if= is_option:= if= opt_key= not= in= quarter_opt:= raise= RuntimeError(= f"No= MarketData= option= prices= for= {opt_key}= in= "= f"period= {q}")= ticker_opt=quarter_opt[opt_key] opt_dates=sorted(d for= d= in= ticker_opt= if= d=>= period_start) if not opt_dates: raise RuntimeError( f"MarketData option prices for {opt_key} in period " f"{q} contain no dates at or after {period_start}") if ticker not in close.columns: raise RuntimeError( f"No underlying close series for {ticker}") src = close[ticker].dropna() avail = src[src.index >= ps] if avail.empty: raise RuntimeError( f"No underlying price for {ticker} at {period_start}") opt_start = ticker_opt[opt_dates[0]] underlying_start = float(avail.iloc[0]) if opt_start<= 0= or= underlying_start= <=0: raise= RuntimeError(= f"Non-positive= starting= price= for= {opt_key}= in= "= f"period= {q}")= start_prices[(ticker,= pos_type)]=opt_start start_underlying[(ticker,= pos_type)]=underlying_start costs[(ticker,= pos_type)]=value *= opt_start= /= underlying_start= exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=True total_cost= +=costs[(ticker, pos_type)]= continue= Plain= stock= in= full= mode= if= ticker= not= in= close.columns:= continue= src=close[ticker].dropna() avail=src[src.index>= ps] if avail.empty: continue stock_start = float(avail.iloc[0]) if stock_start<= 0:= continue= start_prices[(ticker,= pos_type)]=stock_start start_underlying[(ticker,= pos_type)]=stock_start costs[(ticker,= pos_type)]=value exposure[(ticker,= pos_type)]=value use_opt_px[(ticker,= pos_type)]=False total_cost= +=value if= total_cost== 0:= continue= Daily= P&L= relative= to= period= start.= Skip= first= day= of= subsequent= periods= (already= recorded= as= last= day= of= the= prior= period)= to= avoid= duplicate= boundary= dates.= start_idx=1 if= i=> 0 else 0 Forward-fill: track last known option price so that gaps in option data don't cause positions to vanish mid-period. last_opt = {k: v for k, v in start_prices.items() if use_opt_px.get(k)} for day_idx in range(start_idx, len(period_close)): day = period_close.index[day_idx] day_str = day.strftime('%Y-%m-%d') period_pnl = 0 for (ticker, pos_type), value in exposure.items(): p0 = start_prices[(ticker, pos_type)] if p0 == 0: continue if use_opt_px[(ticker, pos_type)]: opt_key = _option_position_key(ticker, pos_type) p1_val = quarter_opt.get(opt_key, {}).get(day_str) if p1_val is not None: last_opt[(ticker, pos_type)] = p1_val else: p1_val = last_opt.get((ticker, pos_type)) if p1_val is None: continue underlying_p0 = start_underlying.get((ticker, pos_type)) if not underlying_p0 or underlying_p0<= 0:= continue= position_pnl=value *= (float(p1_val)= -= p0)= /= underlying_p0= else:= if= ticker= not= in= period_close.columns:= continue= p1_val=period_close[ticker].iloc[day_idx] if= pd.isna(p1_val):= continue= stock_ret=(float(p1_val) -= p0)= /= p0= if= mode== 'equity_only':= position_pnl=( value= *= _linear_underlying_sign(pos_type)= *= stock_ret)= else:= position_pnl=value *= stock_ret= period_pnl= +=position_pnl dates_out.append(day)= values_out.append(cum_growth= *= (1= += period_pnl= /= total_cost))= Chain:= next= period= starts= from= the= last= day's= growth= factor= if= values_out:= cum_growth=values_out[-1] return= dates_out,= values_out= import= plotly.graph_objects= as= go= HUGO_BASE=os.path.expanduser('~/My Drive/repos/stafforini.com')= ──= Fetch= daily= prices= ────────────────────────────────────────────= close=download_daily(all_tickers, first_date,= today)= dates_eq,= vals_eq=daily_cumulative( holdings,= quarters,= filing_dates,= close,= today,= 'equity_only')= ──= Option= proxy= with= representative= notional-matched= options= ───────= option_positions=sorted({ (t,= pt)= for= q= in= quarters= for= (t,= pt)= in= holdings[q]= if= pt= in= ('call',= 'put')= })= per_period_opt=download_option_prices( option_positions,= quarters,= holdings,= filing_dates,= today)= dates_full,= vals_full=daily_cumulative( holdings,= quarters,= filing_dates,= close,= today,= 'full',= per_period_opt=per_period_opt) ──= Compute= SPY= benchmark= ─────────────────────────────────────────= spy_series=close['SPY'].dropna() spy_start=spy_series[spy_series.index>= pd.Timestamp(first_date)] if not spy_start.empty: spy_p0 = float(spy_start.iloc[0]) spy_dates = spy_start.index.tolist() spy_vals = [float(p) / spy_p0 for p in spy_start.values] else: spy_dates, spy_vals = [], [] ── Plot with Plotly ─────────────────────────────────────────────── eq_pct = [round((v - 1) * 100, 1) for v in vals_eq] full_pct = [round((v - 1) * 100, 1) for v in vals_full] spy_pct = [round((v - 1) * 100, 1) for v in spy_vals] fig = go.Figure() fig.add_trace(go.Scatter( x=dates_eq, y=eq_pct, mode='lines', name='Equity proxy', line=dict(color='#2563eb', width=2))) fig.add_trace(go.Scatter( x=dates_full, y=full_pct, mode='lines', name='Option proxy', line=dict(color='#dc2626', width=2))) fig.add_trace(go.Scatter( x=spy_dates, y=spy_pct, mode='lines', name='S&P 500 (SPY)', line=dict(color='#16a34a', width=2, dash='dot'))) Vertical lines at filing dates (rebalancing points) for fd in filing_dates.values(): fig.add_vline(x=fd, line=dict(color='gray', width=0.5), opacity=0.4) fig.add_hline(y=0, line=dict(color='gray', width=0.8)) fig.update_layout( title=dict(text='SA LP copycat: cumulative returns', font=dict(size=15)), yaxis=dict(title='Cumulative return', hoverformat='+.1f', ticksuffix='%'), hovermode='x unified', xaxis=dict(spikemode='across', spikethickness=0.5, spikedash='solid', spikecolor='gray'), template='plotly_white', legend=dict(x=0.02, y=0.98, bgcolor='rgba(255,255,255,0.8)'), margin=dict(l=60, r=20, t=50, b=40), height=500, ) ── Generate HTML with dark-mode support ────────────────────────── import re chart_html = fig.to_html(full_html=False, include_plotlyjs='cdn', config={'responsive': True, 'displayModeBar': False}) div_id = re.search(r'id="([^"]+)"', chart_html).group(1) dark_script = """""" % div_id outpath = os.path.join(HUGO_BASE, 'static', 'images', 'sa-lp-returns.html') with open(outpath, 'w') as f: f.write('\n\n\n' '\n' '\n' '\n\n' + chart_html + dark_script + '\n\n') ```

```text COPYCAT STRATEGY RETURNS ======================================================================== Period Dates Eq. proxy Opt. proxy SPY ------------------------------------------------------------------------ Q4_2024 2025-02-12 to 2025-05-14 -14.73% -14.73% -2.32% Q1_2025 2025-05-14 to 2025-08-14 +24.14% +35.28% +10.09% Q2_2025 2025-08-14 to 2025-11-14 +16.45% +22.37% +4.47% Q3_2025 2025-11-14 to 2026-02-11 +14.54% +19.94% +3.29% Q4_2025 † 2026-02-11 to 2026-04-15 +25.70% +25.14% +1.43% ------------------------------------------------------------------------ Cumulative 2025-02-12 to 2026-04-15 +77.49% +111.90% +17.69% † = partial period (still holding; updates on re-evaluation) Eq. proxy = stocks plus option rows as linear underlying exposure Opt. proxy = options sized to 13F notional; returns on deployed capital RISK-ADJUSTED RETURNS ======================================================= Metric Eq.proxy Opt.proxy SPY ------------------------------------------------------- Ann. volatility 52.4% 61.5% 19.0% Sharpe (rf=4%) 1.13 1.30 0.63 Max drawdown -45.8% -45.8% -18.8% ```