Situational Awareness LP is a hedge fund founded in 2024 by Leopold Aschenbrenner and co-managed by Carl Shulman. It is backed by Patrick & John Collison, Daniel Gross, and Nat Friedman. The fund’s thesis is explicitly AGI-focused, pursuing an opportunistic approach to public equities and strategic investments in semiconductor companies and energy infrastructure.

The copycat approach

Investing directly in the fund requires a $25M minimum, a two-year lockup followed by quarterly redemptions over two more years, and Qualified Purchaser accreditation. The fund also has the standard “2 and 20” fee structure. If those barriers are too much for you, there is an alternative. Institutional investment managers with over $100M in qualifying assets must file Form 13F with the United States Securities and Exchange Commission (SEC) each quarter, disclosing their long equity positions, options, and convertible bonds. Filings are due 45 days after quarter-end and are published on the SEC’sEDGAR system.

This approach has significant limitations, however: the copycat sees each portfolio only after it is disclosed, not when the fund actually trades into it;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

A trader pursuing this strategy would proceed as follows. Each time a 13F filing is published (the “filing date”, typically ~45 days after quarter-end), they would buy the disclosed portfolio at that day’s closing prices, weighting each position by its reported dollar value, and hold it unchanged until the next filing, at which point they would rebalance to match the new disclosure.

We can backtest this strategy by applying it to all existing filings and computing the returns to date. The script below reports compounded returns in two modes,equity proxy andoption proxy, explained below. The final period runs from the most recent filing date to today, so re-evaluating the script updates that partial-period figure automatically.²

# ── 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.importurllib.request,re,json,sys,time,os,xml.etree.ElementTreeasETfromdatetimeimportdatetime# ── 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',}deffetch(url,timeout=10):time.sleep(0.5)req=urllib.request.Request(url,headers={'User-Agent':SEC_UA})withurllib.request.urlopen(req,timeout=timeout)asresp:returnresp.read()deffind_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))forhitindata.get('hits',{}).get('hits',[]):doc_id=hit['_id']# format: "accession:filename"filename=doc_id.split(':',1)[1]if':'indoc_idelse''iffilename.endswith('.xml')and'primary_doc'notinfilename:returnfilenameexceptException:pass# Fallback: filing index pageacc_path=acc.replace('-','')try:html=fetch(f'{BASE}/{acc_path}/{acc}-index.htm').decode()forhrefinre.findall(r'href="([^"]*\.xml)"',html):fn=href.split('/')[-1]iffn!='primary_doc.xml'and'xslForm'notinhref:returnfnexceptException:passreturnNonedefparse_infotable(xml_data):root=ET.fromstring(xml_data)holdings=[]forinfoinroot.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,'')ifnotticker:issuer=info.findtext('ns:nameOfIssuer','',NS)print(f"WARNING: Unknown CUSIP{cusip} ({issuer})",file=sys.stderr)ticker=f"UNKNOWN_{cusip}"pos_type='put'ifputcall=='put'else'call'ifputcall=='call'else'long'holdings.append({"ticker":ticker,"type":pos_type,"value":value})returnholdingsdefquarter_from_filing_date(fdate):d=datetime.strptime(fdate,'%Y-%m-%d')m,y=d.month,d.yearifm<=3:returnf'Q4_{y-1}',f'{y-1}-12-31'elifm<=6:returnf'Q1_{y}',f'{y}-03-31'elifm<=9:returnf'Q2_{y}',f'{y}-06-30'else:returnf'Q3_{y}',f'{y}-09-30'defload_cache():ifos.path.exists(CACHE):withopen(CACHE)asf:returnjson.load(f)return{"filings":[]}defsave_cache(data):os.makedirs(os.path.dirname(CACHE),exist_ok=True)withopen(CACHE,'w')asf:json.dump(data,f)cached=load_cache()cached_quarters={f["quarter"]forfincached["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])foriinrange(len(recent['form']))ifrecent['form'][i]=='13F-HR'])forfdate,accinaccessions:quarter,quarter_end=quarter_from_filing_date(fdate)ifquarterincached_quarters:continuefilename=find_infotable_filename(acc)ifnotfilename:print(f"Could not find infotable for{acc}",file=sys.stderr)continueacc_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=lambdaf:f["filing_date"])save_cache(result)exceptExceptionase:ifresult["filings"]:print(f"SEC fetch error ({e}); using cache",file=sys.stderr)else:raisereturnjson.dumps(result)

importjsonimportyfinanceasyfimportpandasaspdfromdatetimeimportdatetime,timedeltaimportnumpyasnpimportrequestsimporttimeimportosimportwarningswarnings.filterwarnings('ignore')# Parse data from the scraper blockparsed=json.loads(data)ifisinstance(data,str)elsedatafilings=parsed["filings"]# Build internal structuresfiling_dates={f["quarter"]:f["filing_date"]forfinfilings}quarter_end_dates={f["quarter"]:f["quarter_end"]forfinfilings}quarters=[f["quarter"]forfinfilings]# 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={}forfinfilings:positions={}forhinf["holdings"]:ticker=h["ticker"]pos_type=h["type"]value=h["value"]key=(ticker,pos_type)positions[key]=positions.get(key,0)+valueholdings[f["quarter"]]=positionsdef_extract_close_series(df,ticker):"""Extract a single close-price series from a yfinance result."""ifdf.empty:returnpd.Series(dtype=float)ifisinstance(df.columns,pd.MultiIndex):if'Close'notindf.columns.get_level_values(0):returnpd.Series(dtype=float)close=df['Close']ifisinstance(close,pd.DataFrame):iftickerinclose.columns:series=close[ticker]eliflen(close.columns)==1:series=close.iloc[:,0]else:returnpd.Series(dtype=float)else:series=closeelif'Close'indf.columns:series=df['Close']ifisinstance(series,pd.DataFrame):series=series.iloc[:,0]else:returnpd.Series(dtype=float)returnpd.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)defget_prices(tickers,dates):"""Fetch close prices for tickers on specific dates."""unique_tickers=sorted(set(tickers))all_dates=[datetime.strptime(d,'%Y-%m-%d')fordindates]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 tickersifdf.empty:close=pd.DataFrame()elifisinstance(df.columns,pd.MultiIndex)and'Close'indf.columns.get_level_values(0):close=df['Close'].copy()elif'Close'indf.columns:close=df[['Close']].copy()close.columns=unique_tickerselse:close=pd.DataFrame()prices={}fortickerinunique_tickers:iftickerinclose.columns:series=pd.to_numeric(close[ticker],errors='coerce').dropna()else:series=pd.Series(dtype=float)ifseries.empty:series=_download_close_series(ticker,start,end)ifseries.empty:continueprices[ticker]={}fordate_strindates:target=pd.Timestamp(datetime.strptime(date_str,'%Y-%m-%d'))after=series[series.index>=target]ifnotafter.empty:prices[ticker][date_str]=float(after.iloc[0])else:before=series[series.index<=target]ifnotbefore.empty:prices[ticker][date_str]=float(before.iloc[-1])returnpricesdef_price_on_or_after(px_by_date,target_date):"""Return (date, price) for the first available price on/after target."""ifnotpx_by_date:returnNonedates=sorted(dfordinpx_by_dateifd>=target_date)ifnotdates:returnNoned=dates[0]returnd,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."""ifnotpx_by_date:returnNonedates=sorted(dfordinpx_by_dateifd<=target_date)ifnotdates:returnNoned=dates[-1]returnd,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)ifstartisNoneorendisNone:returnNonestart_actual,p0=startend_actual,p1=endifend_actual<start_actual:returnNonereturnstart_actual,end_actual,p0,p1def_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-1ifpos_type=='put'else1defcompute_return(positions,prices,start_date,end_date,mode='equity_only',option_prices=None,option_params=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. """total_cost=0portfolio_pnl=0for(ticker,pos_type),valueinpositions.items():is_option=pos_typein('call','put')stock_px=prices.get(ticker)ifmode=='equity_only':ifpos_typenotin('long','call','put'):continuepair=_period_price_pair(stock_px,start_date,end_date)ifpairisNone:continuestart_actual,end_actual,p0,p1=pairifp0==0:continuestock_ret=(p1-p0)/p0total_cost+=valueportfolio_pnl+=value*_linear_underlying_sign(pos_type)*stock_retcontinueopt_key=_option_position_key(ticker,pos_type)opt_px=option_prices.get(opt_key)ifoption_priceselseNoneuse_option_px=bool(is_optionandopt_px)ifuse_option_px:pair=_period_price_pair(opt_px,start_date,end_date)ifpairisNone:use_option_px=Falseelse:start_actual,end_actual,opt_p0,opt_p1=pairstock_start=_price_on_or_after(stock_px,start_actual)ifstock_startisNoneorstock_start[1]<=0:stock_start=_price_on_or_after(stock_px,start_date)ifstock_startisNoneorstock_start[1]<=0:use_option_px=Falseelse:p0,p1=opt_p0,opt_p1underlying_p0=stock_start[1]ifuse_option_px:ifp0<=0orunderlying_p0<=0:continueposition_cost=value*(p0/underlying_p0)position_pnl=value*((p1-p0)/underlying_p0)else:pair=_period_price_pair(stock_px,start_date,end_date)ifpairisNone:continuestart_actual,end_actual,p0,p1=pairifp0==0:continuestock_ret=(p1-p0)/p0ifis_option:params=option_params.get(ticker)ifoption_paramselseNoneifnotparams:continueK_call,K_put,sigma=paramsK_over_S=K_callifpos_type=='call'elseK_putdt=days_between(start_actual,end_actual)/365.25premium_per_notional=bs_price(1.0,K_over_S,OPTION_T,sigma,pos_type)ifpremium_per_notional<=0:continueposition_cost=value*premium_per_notionalposition_pnl=value*bs_option_pnl_per_notional(stock_ret,K_over_S,OPTION_T,dt,sigma,pos_type)else:position_cost=valueposition_pnl=value*stock_retifposition_cost<=0:continuetotal_cost+=position_costportfolio_pnl+=position_pnlreturnportfolio_pnl/total_costiftotal_costelseNonedefannualize(ret,days):"""Annualize a return over a given number of calendar days."""ifretisNoneordays<=0:returnNonereturn(1+ret)**(365.25/days)-1defdays_between(d1,d2):return(datetime.strptime(d2,'%Y-%m-%d')-datetime.strptime(d1,'%Y-%m-%d')).daysdeffmt(ret):returnf"{ret*100:+.2f}%"ifretisnotNoneelse"N/A"# Collect all tickers and datesall_tickers=set()forpositionsinholdings.values():for(ticker,_)inpositions: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'inpriceselsenext(iter(prices),None)ifnotreforrequested_datenotinprices[ref]:returnrequested_datetarget_price=prices[ref][requested_date]# Re-download a small window to find the real date of this pricestart=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)ifdf.empty:returnrequested_dateifisinstance(df.columns,pd.MultiIndex):close=df['Close'][ref].dropna()elif'Close'indf.columns:close=df['Close'].dropna()else:close=df.iloc[:,0].dropna()fordt,pxinclose.items():val=float(px.iloc[0])ifisinstance(px,pd.Series)elsefloat(px)ifabs(val-target_price)<0.01:ts=dt[0]ifisinstance(dt,tuple)elsedtreturnpd.Timestamp(ts).strftime('%Y-%m-%d')returnrequested_datetoday_resolved=_resolve_price_date(prices,today)iftoday_resolved!=today:fortickerinprices:iftodayinprices[ticker]:prices[ticker][today_resolved]=prices[ticker].pop(today)today=today_resolveddefdownload_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)ifdf.empty:close=pd.DataFrame()elifisinstance(df.columns,pd.MultiIndex)and'Close'indf.columns.get_level_values(0):close=df['Close'].copy()elif'Close'indf.columns:close=df[['Close']].copy()close.columns=tickers_sortedelse:close=pd.DataFrame()fortickerintickers_sorted:iftickerinclose.columnsandnotclose[ticker].dropna().empty:continueseries=_download_close_series(ticker,start,end)ifnotseries.empty:close[ticker]=seriesreturnclose.sort_index()# -- Historical option prices via MarketData / Alpha Vantage ----------------OPTION_CACHE_DIR=os.path.expanduser('~/My Drive/notes/.sa-lp-option-cache')_AV_BASE='https://www.alphavantage.co/query'_AV_RATE_DELAY=0.85# seconds between requests (75 req/min limit)_MD_BASE='https://api.marketdata.app/v1'_MD_RATE_DELAY=0.15OPTION_CACHE_COLUMNS=['date','selected_on','option_type','symbol','strike','expiry','delta','price']def_normalize_option_type(option_type):option_type=str(option_type).lower()ifoption_typenotin('call','put'):raiseValueError(f"Unsupported option type:{option_type}")returnoption_typedef_empty_option_cache():returnpd.DataFrame(columns=OPTION_CACHE_COLUMNS)def_option_cache_path(ticker,option_type,legacy=False):iflegacy:returnos.path.join(OPTION_CACHE_DIR,f'{ticker}.csv')option_type=_normalize_option_type(option_type)returnos.path.join(OPTION_CACHE_DIR,f'{ticker}-{option_type}.csv')def_load_option_cache(ticker,option_type,include_legacy=True):"""Load cached option data for a ticker/type. Returns DataFrame or empty."""option_type=_normalize_option_type(option_type)primary_path=_option_cache_path(ticker,option_type)paths=[primary_path]# Pre-fix caches were call-only and named TICKER.csv. They are safe to# reuse for calls but must not be reused for puts. Once a typed cache# exists, prefer it; typed MarketData rows record selected_on and avoid# roll-date ambiguity in the legacy cache.if(include_legacyandoption_type=='call'andnotos.path.exists(primary_path)):legacy_path=_option_cache_path(ticker,option_type,legacy=True)iflegacy_pathnotinpaths:paths.append(legacy_path)frames=[]forpathinpaths:ifnotos.path.exists(path):continuedf=pd.read_csv(path)ifdf.empty:continueif'option_type'notindf.columns:df['option_type']='call'forcolinOPTION_CACHE_COLUMNS:ifcolnotindf.columns:df[col]=np.nanforcolin('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()frames.append(df[OPTION_CACHE_COLUMNS])ifnotframes:return_empty_option_cache()cache=pd.concat(frames,ignore_index=True)cache=cache[cache['option_type']==option_type].copy()cache.dropna(subset=['date'],inplace=True)forcolin('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)returncache[OPTION_CACHE_COLUMNS]def_save_option_cache(ticker,option_type,df):"""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)ifdf.empty:df=_empty_option_cache()else:df=df.copy()df['option_type']=option_typeforcolinOPTION_CACHE_COLUMNS:ifcolnotindf.columns:df[col]=np.nandf.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_fetch_option_chain(ticker,date_str,api_key):"""Fetch the full option chain for ticker on a given date from Alpha Vantage."""params={'function':'HISTORICAL_OPTIONS','symbol':ticker,'date':date_str,'apikey':api_key,}try:resp=requests.get(_AV_BASE,params=params,timeout=30)resp.raise_for_status()body=resp.json()returnbody.get('data',[])exceptException:return[]def_contract_window(ref_date_str):ref=datetime.strptime(ref_date_str,'%Y-%m-%d')returnref+timedelta(days=270),ref+timedelta(days=456)def_contract_from_cache_row(row,ref_date_str,option_type):option_type=_normalize_option_type(option_type)ifstr(row.get('option_type',option_type)).lower()!=option_type:returnNonelo,hi=_contract_window(ref_date_str)try:exp=datetime.strptime(str(row['expiry']),'%Y-%m-%d')except(KeyError,TypeError,ValueError):returnNoneifnot(lo<=exp<=hi):returnNonestrike=_safe_float(row.get('strike'))delta=_safe_float(row.get('delta'))price=_safe_float(row.get('price'))ifstrikeisNoneordeltaisNoneorpriceisNoneorprice<=0:returnNonereturn{'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,require_selected=False):rows=cache[(cache['date']==ref_date_str)&(cache['option_type']==option_type)]selected_rows=rows[rows['selected_on']==ref_date_str]ifnotselected_rows.empty:rows=selected_rowselifrequire_selected:rows=selected_rowscandidates=[]for_,rowinrows.iterrows():contract=_contract_from_cache_row(row,ref_date_str,option_type)ifcontract:candidates.append(contract)ifnotcandidates:returnNonecandidates.sort(key=lambdax:abs(abs(x['delta'])-OPTION_DELTA))returncandidates[0]def_select_best_contract(chain,ref_date_str,option_type):"""Select the best-matching call/put contract from an option chain. Criteria: matching option type, expiry 9-15 months from ref_date, absolute delta closest to 0.15. Returns dict with type, strike, expiry, delta, price or None. """option_type=_normalize_option_type(option_type)lo,hi=_contract_window(ref_date_str)candidates=[]forcinchain:ifc.get('type','').lower()!=option_type:continuetry:exp=datetime.strptime(c['expiration'],'%Y-%m-%d')except(KeyError,ValueError):continueifnot(lo<=exp<=hi):continuedelta=_safe_float(c.get('delta'))ifdeltaisNone:continueabs_delta=abs(delta)ifabs_delta==0:continue# Price: prefer mid if available, else lastprice=_parse_option_price(c)ifpriceisNoneorprice<=0:continuecandidates.append({'option_type':option_type,'strike':float(c['strike']),'expiry':c['expiration'],'delta':delta,'price':price,})ifnotcandidates:returnNone# Pick contract with absolute delta closest to 0.15.candidates.sort(key=lambdax:abs(abs(x['delta'])-OPTION_DELTA))returncandidates[0]def_parse_option_price(contract):"""Extract a mark price from an option contract record."""mid=_safe_float(contract.get('mid'))ifmidandmid>0:returnmidbid=_safe_float(contract.get('bid'))ask=_safe_float(contract.get('ask'))last=_safe_float(contract.get('last'))ifbidandaskandbid>0andask>0:return(bid+ask)/2iflastandlast>0:returnlastreturnNonedef_safe_float(val):try:out=float(val)ifnp.isnan(out):returnNonereturnoutexcept(TypeError,ValueError):returnNonedef_option_provider():requested=os.environ.get('OPTION_DATA_PROVIDER','').strip().lower()ifrequestedin('marketdata','md'):return'marketdata'ifrequestedin('alphavantage','alpha_vantage','av'):return'alphavantage'ifrequestedin('cache','cached','none'):return'cache'if_marketdata_key():return'marketdata'ifos.environ.get('ALPHA_VANTAGE_KEY',''):return'alphavantage'return'cache'def_marketdata_key():return(os.environ.get('MARKETDATA_KEY','')oros.environ.get('MARKETDATA_API_KEY',''))def_marketdata_get(path,params,api_key):"""Fetch a MarketData endpoint, returning normalized row dictionaries."""ifnotapi_key:return[]headers={'Accept':'application/json','Authorization':f'Bearer{api_key}'}try:resp=requests.get(_MD_BASE+path,params=params,headers=headers,timeout=30)resp.raise_for_status()body=resp.json()exceptException:return[]ifbody.get('s')!='ok':return[]lengths=[len(v)forvinbody.values()ifisinstance(v,list)]n=max(lengths)iflengthselse0rows=[]foriinrange(n):row={}forkey,valinbody.items():ifisinstance(val,list):row[key]=val[i]ifi<len(val)elseNoneelse:row[key]=valrows.append(row)returnrowsdef_marketdata_date(timestamp):try:returndatetime.utcfromtimestamp(int(timestamp)).strftime('%Y-%m-%d')except(TypeError,ValueError,OSError):returnNonedef_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('.','')returnf'{root}{exp}{cp}{strike_int:08d}'def_fetch_marketdata_chain(ticker,date_str,option_type,api_key):lo,hi=_contract_window(date_str)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)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={}forrowinrows:date_str=_marketdata_date(row.get('updated'))ifnotdate_str:continueprice=_parse_option_price(row)ifpriceisnotNoneandprice>0:prices[date_str]=pricereturnpricesdef_implied_vol_from_price(S,K,T,option_price,option_type):"""Infer Black-Scholes volatility from an observed option mid price."""ifany(xisNoneforxin(S,K,T,option_price)):returnNoneifS<=0orK<=0orT<=0oroption_price<=0:returnNoneintrinsic=max(S-K,0)ifoption_type=='call'elsemax(K-S,0)upper=Sifoption_type=='call'elseKifoption_price<intrinsic-1e-6oroption_price>upper*1.5:returnNonelo,hi=1e-4,5.0try:if(option_price<bs_price(S,K,T,lo,option_type)-1e-4oroption_price>bs_price(S,K,T,hi,option_type)+1e-4):returnNonefor_inrange(80):mid=(lo+hi)/2ifbs_price(S,K,T,mid,option_type)<option_price:lo=midelse:hi=midreturn(lo+hi)/2except(FloatingPointError,ValueError,ZeroDivisionError):returnNonedef_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'))ifnativeisnotNoneandnative!=0:returnnativeS=_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'))ifsigmaisNoneorsigma<=0:sigma=_implied_vol_from_price(S,K,T,price,option_type)ifSisNoneorKisNoneorsigmaisNoneorsigma<=0:returnNonereturnbs_delta(S,K,T,sigma,option_type)def_select_marketdata_contract(chain,ref_date_str,option_type):option_type=_normalize_option_type(option_type)lo,hi=_contract_window(ref_date_str)candidates=[]forcinchain:ifstr(c.get('side','')).lower()!=option_type:continueexpiry=_marketdata_date(c.get('expiration'))ifnotexpiry:continueexp=datetime.strptime(expiry,'%Y-%m-%d')ifnot(lo<=exp<=hi):continueprice=_parse_option_price(c)ifpriceisNoneorprice<=0:continuedelta=_marketdata_delta(c,ref_date_str,expiry,option_type,price)ifdeltaisNoneordelta==0:continuestrike=_safe_float(c.get('strike'))symbol=c.get('optionSymbol')ifstrikeisNoneornotsymbol:continuecandidates.append({'option_type':option_type,'symbol':symbol,'strike':strike,'expiry':expiry,'delta':delta,'price':price,})ifnotcandidates:returnNonecandidates.sort(key=lambdax:abs(abs(x['delta'])-OPTION_DELTA))returncandidates[0]def_cached_contract_price(cache,option_type,date_str,strike,expiry):rows=cache[(cache['date']==date_str)&(cache['option_type']==option_type)&(abs(cache['strike']-strike)<0.01)&(cache['expiry'].astype(str)==str(expiry))]for_,rowinrows.iterrows():price=_safe_float(row.get('price'))ifpriceisnotNoneandprice>0:returnpricereturnNonedef_extract_contract_price(chain,strike,expiry,option_type):"""Find the price of a specific option contract."""option_type=_normalize_option_type(option_type)forcinchain:ifc.get('type','').lower()!=option_type:continuetry:if(abs(float(c['strike'])-strike)<0.01andc.get('expiration')==expiry):return_parse_option_price(c)except(KeyError,TypeError,ValueError):continuereturnNonedefdownload_option_prices(option_positions,quarters,holdings,filing_dates,today):"""Download historical representative option prices. MarketData is preferred when MARKETDATA_KEY is present. Its historical chains on the Starter plan expose raw quotes but not usable historical Greeks, so contract selection uses vendor delta when available and otherwise infers Black-Scholes delta from the option mid price. Alpha Vantage remains available via ALPHA_VANTAGE_KEY. For each (ticker, option_type) and each filing period: 1. On the first trading day, select the contract matching type, expiring 9-15 months out, with |delta| closest to 0.15. 2. Lock in that contract for the period. 3. Track its historical market price through the period. 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, avoiding cross-contract mixing at boundary dates where one period ends and the next begins. fallback_positions : set Option positions where no option data was found (need BS fallback). """option_positions=sorted({(ticker,_normalize_option_type(pos_type))forticker,pos_typeinoption_positions})provider=_option_provider()md_key=_marketdata_key()av_key=os.environ.get('ALPHA_VANTAGE_KEY','')ifprovider=='marketdata'andnotmd_key:provider='cache'ifprovider=='alphavantage'andnotav_key:provider='cache'cache_only=provider=='cache'ifcache_only:importsysprint("WARNING: no option-data key set; using cached option prices ""where available and BS repricing elsewhere.",file=sys.stderr)os.makedirs(OPTION_CACHE_DIR,exist_ok=True)per_period={}# {q: {(ticker, type): {date_str: price}}}fallback=set()fetched={'marketdata_chains':0,'marketdata_quotes':0,'alphavantage_chains':0}forticker,option_typeinoption_positions:opt_key=_option_position_key(ticker,option_type)# When MarketData is available, ignore legacy untyped call-only caches# so stale Alpha Vantage selections do not suppress a fresh selection.include_legacy=provider!='marketdata'cache=_load_option_cache(ticker,option_type,include_legacy=include_legacy)position_has_data=Falsenew_rows=[]fori,qinenumerate(quarters):# Skip quarters where this exact option position is absent.has_opts=opt_keyinholdings[q]ifnothas_opts:continueperiod_start=filing_dates[q]period_end=(filing_dates[quarters[i+1]]ifi<len(quarters)-1elsetoday)trading_days=pd.bdate_range(period_start,period_end)iflen(trading_days)==0:continuefirst_day=trading_days[0].strftime('%Y-%m-%d')# -- Select contract on first trading day --contract=_select_cached_contract(cache,option_type,first_day,require_selected=(provider=='marketdata'))ifcontract:from_cache=Trueelse:from_cache=Falseifprovider=='marketdata':time.sleep(_MD_RATE_DELAY)chain=_fetch_marketdata_chain(ticker,first_day,option_type,md_key)fetched['marketdata_chains']+=1contract=_select_marketdata_contract(chain,first_day,option_type)elifprovider=='alphavantage':time.sleep(_AV_RATE_DELAY)chain=_fetch_option_chain(ticker,first_day,av_key)fetched['alphavantage_chains']+=1contract=_select_best_contract(chain,first_day,option_type)ifcontract: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'],})ifcontractisNone:fallback.add(opt_key)continuestrike=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]ifnotselected_rows.empty:rows=selected_rowsfor_,rowinrows.iterrows():period_prices[row['date']]=float(row['price'])ifprovider=='marketdata':needs_fetch=(md_keyand(notperiod_pricesormax(period_prices)<period_end))ifneeds_fetch:time.sleep(_MD_RATE_DELAY)quote_prices=_fetch_marketdata_quotes(symbol,period_start,period_end,md_key)fetched['marketdata_quotes']+=1forday_str,priceinquote_prices.items():ifperiod_start<=day_str<=period_end:period_prices[day_str]=pricenew_rows.append({'date':day_str,'selected_on':first_day,'option_type':option_type,'symbol':symbol,'strike':strike,'expiry':expiry,'delta':contract['delta'],'price':price,})ifcontract.get('price')andfirst_daynotinperiod_prices:period_prices[first_day]=contract['price']elifprovider=='alphavantage'andnotfrom_cache:# Alpha Vantage historical options are fetched as daily chains.ifcontract.get('price'):period_prices[first_day]=contract['price']fordayintrading_days[1:]:day_str=day.strftime('%Y-%m-%d')cached_price=_cached_contract_price(cache,option_type,day_str,strike,expiry)ifcached_priceisnotNone:period_prices[day_str]=cached_pricecontinuetime.sleep(_AV_RATE_DELAY)chain=_fetch_option_chain(ticker,day_str,av_key)fetched['alphavantage_chains']+=1price=_extract_contract_price(chain,strike,expiry,option_type)ifpriceisnotNone:period_prices[day_str]=pricenew_rows.append({'date':day_str,'selected_on':first_day,'option_type':option_type,'symbol':symbol,'strike':strike,'expiry':expiry,'delta':contract['delta'],'price':price,})# Accumulate per-period pricesifperiod_prices:per_period.setdefault(q,{})[opt_key]=period_pricesposition_has_data=True# Persist new data to cacheifnew_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)ifnotposition_has_data:fallback.add(opt_key)ifany(fetched.values()):importsysparts=[]iffetched['marketdata_chains']:parts.append(f"{fetched['marketdata_chains']} MarketData chains")iffetched['marketdata_quotes']:parts.append(f"{fetched['marketdata_quotes']} MarketData quote series")iffetched['alphavantage_chains']:parts.append(f"{fetched['alphavantage_chains']} Alpha Vantage chains")print(f"[options] Fetched{', '.join(parts)}",file=sys.stderr)returnper_period,fallback# -- Fallback: Black-Scholes repricing for tickers without option data -----fromscipy.statsimportnormas_normdefcompute_realized_vol(tickers,download_daily_fn,today_str):"""Compute annualized realized vol from trailing 1-year daily returns."""vol_start=(datetime.strptime(today_str,'%Y-%m-%d')-timedelta(days=400)).strftime('%Y-%m-%d')vol_df=download_daily_fn(tickers,vol_start,today_str)result={}fortickerintickers:iftickerinvol_df.columns:series=vol_df[ticker].dropna()iflen(series)>20:log_rets=np.log(series/series.shift(1)).dropna().tail(252)result[ticker]=float(log_rets.std()*np.sqrt(252))returnresultdefbs_price(S,K,T,sigma,option_type='call'):"""Black-Scholes option price (assumes zero risk-free rate and dividends)."""ifT<=0orsigma<=0:ifoption_type=='call':returnmax(S-K,0)returnmax(K-S,0)d1=(np.log(S/K)+(sigma**2/2)*T)/(sigma*np.sqrt(T))d2=d1-sigma*np.sqrt(T)ifoption_type=='call':returnS*_norm.cdf(d1)-K*_norm.cdf(d2)returnK*_norm.cdf(-d2)-S*_norm.cdf(-d1)defbs_delta(S,K,T,sigma,option_type='call'):"""Black-Scholes delta (assumes zero risk-free rate and dividends)."""ifT<=0orsigma<=0:ifoption_type=='call':return1.0ifS>Kelse0.0return-1.0ifS<Kelse0.0d1=(np.log(S/K)+(sigma**2/2)*T)/(sigma*np.sqrt(T))ifoption_type=='call':return_norm.cdf(d1)return_norm.cdf(d1)-1defbs_option_return(stock_return,K_over_S,T,delta_t,sigma,option_type='call'):"""Compute option return from stock return using Black-Scholes repricing. Normalizes S_0 = 1, so S_1 = 1 + stock_return and K = K_over_S. T is time to expiry at period start; delta_t is time elapsed. """V0=bs_price(1.0,K_over_S,T,sigma,option_type)V1=bs_price(1.0+stock_return,K_over_S,max(T-delta_t,0),sigma,option_type)ifV0<=0:returnstock_returnreturnV1/V0-1defbs_option_value_per_notional(stock_return,K_over_S,T,delta_t,sigma,option_type='call'):"""Option value per $1 of starting underlying notional."""returnbs_price(1.0+stock_return,K_over_S,max(T-delta_t,0),sigma,option_type)defbs_option_pnl_per_notional(stock_return,K_over_S,T,delta_t,sigma,option_type='call'):"""Option P&L per $1 of starting underlying notional."""v0=bs_price(1.0,K_over_S,T,sigma,option_type)v1=bs_option_value_per_notional(stock_return,K_over_S,T,delta_t,sigma,option_type)returnv1-v0OPTION_DELTA=0.15OPTION_T=1.0defbuild_option_params(option_tickers,ticker_vol):"""Build {ticker: (K_over_S_call, K_over_S_put, sigma)} for BS fallback."""result={}fortinoption_tickers:iftnotinticker_vol:continuesigma=ticker_vol[t]srt=sigma*OPTION_T**0.5# Call: delta = N(d1) = OPTION_DELTAd1_call=_norm.ppf(OPTION_DELTA)K_call=np.exp(-d1_call*srt+sigma**2*OPTION_T/2)# Put: delta = N(d1) - 1 = -OPTION_DELTA, so N(d1) = 1 - OPTION_DELTAd1_put=_norm.ppf(1-OPTION_DELTA)K_put=np.exp(-d1_put*srt+sigma**2*OPTION_T/2)result[t]=(K_call,K_put,sigma)returnresultdefdaily_cumulative(holdings,quarters,filing_dates,close,today,mode,per_period_opt=None,option_params=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, but returns are divided by estimated deployed capital: stock value plus option premium cost. """cum_growth=1.0dates_out=[]values_out=[]fori,qinenumerate(quarters):period_start=filing_dates[q]period_end=filing_dates[quarters[i+1]]ifi<len(quarters)-1elsetodayps=pd.Timestamp(period_start)pe=pd.Timestamp(period_end)# Trading days in this periodmask=(close.index>=ps)&(close.index<=pe)period_close=close[mask]ifperiod_close.empty:continue# Option prices for this period (keyed by (ticker, type) → prices)quarter_opt=per_period_opt.get(q,{})ifper_period_optelse{}# Determine starting prices, fixed exposure, and deployed capital.positions=holdings[q]exposure={}costs={}start_prices={}start_underlying={}bs_params_used={}use_opt_px={}# track which positions use option pricestotal_cost=0for(ticker,pos_type),valueinpositions.items():# Select price source for this positionis_option=pos_typein('call','put')opt_key=_option_position_key(ticker,pos_type)has_opt=is_optionandopt_keyinquarter_optifmode=='equity_only':ifpos_typenotin('long','call','put'):continueiftickernotinclose.columns:continuesrc=close[ticker].dropna()avail=src[src.index>=ps]ifavail.empty:continuestock_start=float(avail.iloc[0])ifstock_start<=0:continuestart_prices[(ticker,pos_type)]=stock_startstart_underlying[(ticker,pos_type)]=stock_startcosts[(ticker,pos_type)]=valueexposure[(ticker,pos_type)]=valueuse_opt_px[(ticker,pos_type)]=Falsetotal_cost+=valuecontinueifhas_opt:ticker_opt=quarter_opt[opt_key]opt_dates=sorted(dfordinticker_optifd>=period_start)ifnotopt_dates:continueiftickernotinclose.columns:continuesrc=close[ticker].dropna()avail=src[src.index>=ps]ifavail.empty:continueopt_start=ticker_opt[opt_dates[0]]underlying_start=float(avail.iloc[0])ifopt_start<=0orunderlying_start<=0:continuestart_prices[(ticker,pos_type)]=opt_startstart_underlying[(ticker,pos_type)]=underlying_startcosts[(ticker,pos_type)]=value*opt_start/underlying_starteliftickerinclose.columns:src=close[ticker].dropna()avail=src[src.index>=ps]ifavail.empty:continuestock_start=float(avail.iloc[0])ifstock_start<=0:continuestart_prices[(ticker,pos_type)]=stock_startstart_underlying[(ticker,pos_type)]=stock_startifis_option:params=option_params.get(ticker)ifoption_paramselseNoneifnotparams:continueK_call,K_put,sigma=paramsK_over_S=K_callifpos_type=='call'elseK_putpremium_per_notional=bs_price(1.0,K_over_S,OPTION_T,sigma,pos_type)ifpremium_per_notional<=0:continuecosts[(ticker,pos_type)]=value*premium_per_notionalbs_params_used[(ticker,pos_type)]=(K_over_S,sigma,premium_per_notional)else:costs[(ticker,pos_type)]=valueelse:continueexposure[(ticker,pos_type)]=valueuse_opt_px[(ticker,pos_type)]=has_opttotal_cost+=costs[(ticker,pos_type)]iftotal_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=1ifi>0else0# Forward-fill: track last known option price so that gaps in# option data don't cause positions to vanish mid-period.last_opt={k:vfork,vinstart_prices.items()ifuse_opt_px.get(k)}forday_idxinrange(start_idx,len(period_close)):day=period_close.index[day_idx]day_str=day.strftime('%Y-%m-%d')period_pnl=0for(ticker,pos_type),valueinexposure.items():p0=start_prices[(ticker,pos_type)]ifp0==0:continueifuse_opt_px[(ticker,pos_type)]:opt_key=_option_position_key(ticker,pos_type)p1_val=quarter_opt.get(opt_key,{}).get(day_str)ifp1_valisnotNone:last_opt[(ticker,pos_type)]=p1_valelse:p1_val=last_opt.get((ticker,pos_type))ifp1_valisNone:continueeliftickerinperiod_close.columns:p1_val=period_close[ticker].iloc[day_idx]ifpd.isna(p1_val):continueelse:continueifuse_opt_px[(ticker,pos_type)]:underlying_p0=start_underlying.get((ticker,pos_type))ifnotunderlying_p0orunderlying_p0<=0:continueposition_pnl=value*(float(p1_val)-p0)/underlying_p0elifmode=='equity_only':stock_ret=(float(p1_val)-p0)/p0position_pnl=(value*_linear_underlying_sign(pos_type)*stock_ret)else:ifpos_typein('call','put'):params=bs_params_used.get((ticker,pos_type))ifnotparams:continueK_over_S,sigma,premium_per_notional=paramsstock_ret=(float(p1_val)-p0)/p0dt=(day-ps).days/365.25opt_val=bs_option_value_per_notional(stock_ret,K_over_S,OPTION_T,dt,sigma,pos_type)position_pnl=value*(opt_val-premium_per_notional)else:position_pnl=value*(float(p1_val)-p0)/p0period_pnl+=position_pnldates_out.append(day)values_out.append(cum_growth*(1+period_pnl/total_cost))# Chain: next period starts from the last day's growth factorifvalues_out:cum_growth=values_out[-1]returndates_out,values_out# Download representative option prices; fall back to BS repricing for gaps.option_positions=sorted({(t,pt)forqinquartersfor(t,pt)inholdings[q]ifptin('call','put')})option_tickers=sorted({tfort,_inoption_positions})per_period_opt,fallback_positions=download_option_prices(option_positions,quarters,holdings,filing_dates,today)ticker_vol=compute_realized_vol(option_tickers,download_daily,today)params_dict=build_option_params(option_tickers,ticker_vol)# Compute copycat returnsheader=(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=1.0cum_full=1.0cum_spy=1.0fori,qinenumerate(quarters):start=filing_dates[q]end=filing_dates[quarters[i+1]]ifi<len(quarters)-1elsetodaysuffix=" †"ifi==len(quarters)-1else""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,{}),option_params=params_dict)ret_spy=Noneif'SPY'inpricesandstartinprices['SPY']andendinprices['SPY']:spy_p0,spy_p1=prices['SPY'][start],prices['SPY'][end]ifspy_p0!=0:ret_spy=(spy_p1-spy_p0)/spy_p0ifret_eqisnotNone:cum_eq*=(1+ret_eq)ifret_fullisnotNone:cum_full*=(1+ret_full)ifret_spyisnotNone: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-1cum_full_ret=cum_full-1cum_spy_ret=cum_spy-1dates_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)defdaily_returns_from_cumulative(mode,per_period_opt=None,option_params=None):ifdaily_close.empty:returnpd.Series(dtype=float)dates,values=daily_cumulative(holdings,quarters,filing_dates,daily_close,today,mode,per_period_opt=per_period_opt,option_params=option_params)ifnotdates:returnpd.Series(dtype=float)growth=pd.Series(values,index=dates)returngrowth.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,option_params=params_dict)if'SPY'indaily_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)defsharpe(daily_rets,rf_annual=0.04):ifdaily_rets.empty:returnfloat('nan')rf_daily=(1+rf_annual)**(1/252)-1excess=daily_rets-rf_dailyifexcess.std()==0orpd.isna(excess.std()):returnfloat('nan')returnfloat(excess.mean()/excess.std()*252**0.5)defmax_drawdown(daily_rets):ifdaily_rets.empty:returnfloat('nan')cum=(1+daily_rets).cumprod()returnfloat(((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}%")