Data Reference

ohlcv

ohlcv(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: MultiIndex DataFrame (date, symbol) with columns: open, high, low, close, volume, adjusted_close (float).

Example (synthetic data)

ctx.data.ohlcv(["SYN_001", "SYN_002"], lookback=3)
#                    open    high     low   close  volume  adjusted_close
# date       symbol
# 2025-01-02 SYN_001  100.0  102.5   99.1  101.3  1500000         101.3
# 2025-01-02 SYN_002   45.2   46.8   44.9   46.1   800000          46.1

returns

returns(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are daily close-to-close returns (float).

Example (synthetic data)

ctx.data.returns(["SYN_001", "SYN_002"], lookback=5)
#            SYN_001   SYN_002
# 2025-01-06   0.012    -0.004
# 2025-01-07  -0.003     0.008

close

close(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are close (float).

Example (synthetic data)

ctx.data.close(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.0
# 2025-01-07   101.2

open

open(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are open (float).

Example (synthetic data)

ctx.data.open(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.0
# 2025-01-07   101.2

high

high(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are high (float).

Example (synthetic data)

ctx.data.high(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.0
# 2025-01-07   101.2

low

low(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are low (float).

Example (synthetic data)

ctx.data.low(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.0
# 2025-01-07   101.2

volume

volume(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame indexed by date; columns are symbols; values are volume (float).

Example (synthetic data)

ctx.data.volume(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.0
# 2025-01-07   101.2

vwap

vwap(symbols=None, lookback=None, start_date=None, end_date=None) -> DataFrame

Returns: DataFrame of volume-weighted average price per symbol per day (computed from OHLCV if vwap column absent).

Example (synthetic data)

ctx.data.vwap(["SYN_001"], lookback=2)
#            SYN_001
# 2025-01-06   100.5
# 2025-01-07   101.0

fundamentals

fundamentals(symbol: str, dataset: str = "key_metrics_ttm") -> dict | None

Returns: Dictionary of field name → numeric value for the requested dataset, or None if unavailable.

Example (synthetic data)

ctx.data.fundamentals("SYN_001", "key_metrics_ttm")
# {"peRatioTTM": 18.5, "returnOnEquityTTM": 0.22, "debtToEquityTTM": 0.45, ...}

fundamentals datasets

key_metrics_ttm (57 fields)

revenuePerShareTTM, netIncomePerShareTTM, operatingCashFlowPerShareTTM, freeCashFlowPerShareTTM, cashPerShareTTM, bookValuePerShareTTM, tangibleBookValuePerShareTTM, shareholdersEquityPerShareTTM, interestDebtPerShareTTM, marketCapTTM, enterpriseValueTTM, peRatioTTM, priceToSalesRatioTTM, pocfratioTTM, pfcfRatioTTM, pbRatioTTM, ptbRatioTTM, evToSalesTTM, enterpriseValueOverEBITDATTM, evToOperatingCashFlowTTM, evToFreeCashFlowTTM, earningsYieldTTM, freeCashFlowYieldTTM, debtToEquityTTM, debtToAssetsTTM, netDebtToEBITDATTM, currentRatioTTM, interestCoverageTTM, incomeQualityTTM, dividendYieldTTM, payoutRatioTTM, salesGeneralAndAdministrativeToRevenueTTM, researchAndDevelopmentToRevenueTTM, intangiblesToTotalAssetsTTM, capexToOperatingCashFlowTTM, capexToRevenueTTM, capexToDepreciationTTM, stockBasedCompensationToRevenueTTM, grahamNumberTTM, roicTTM, returnOnTangibleAssetsTTM, grahamNetNetTTM, workingCapitalTTM, tangibleAssetValueTTM, netCurrentAssetValueTTM, investedCapitalTTM, averageReceivablesTTM, averagePayablesTTM, averageInventoryTTM, daysSalesOutstandingTTM, daysPayablesOutstandingTTM, daysOfInventoryOnHandTTM, receivablesTurnoverTTM, payablesTurnoverTTM, inventoryTurnoverTTM, roeTTM, capexPerShareTTM

ratios_ttm (69 fields)

dividendYielTTM, dividendYielPercentageTTM, peRatioTTM, pegRatioTTM, payoutRatioTTM, currentRatioTTM, quickRatioTTM, cashRatioTTM, daysOfSalesOutstandingTTM, daysOfInventoryOutstandingTTM, operatingCycleTTM, daysOfPayablesOutstandingTTM, cashConversionCycleTTM, grossProfitMarginTTM, operatingProfitMarginTTM, pretaxProfitMarginTTM, netProfitMarginTTM, effectiveTaxRateTTM, returnOnAssetsTTM, returnOnEquityTTM, returnOnCapitalEmployedTTM, netIncomePerEBTTTM, ebtPerEbitTTM, ebitPerRevenueTTM, debtRatioTTM, debtEquityRatioTTM, longTermDebtToCapitalizationTTM, totalDebtToCapitalizationTTM, interestCoverageTTM, cashFlowToDebtRatioTTM, companyEquityMultiplierTTM, receivablesTurnoverTTM, payablesTurnoverTTM, inventoryTurnoverTTM, fixedAssetTurnoverTTM, assetTurnoverTTM, operatingCashFlowPerShareTTM, freeCashFlowPerShareTTM, cashPerShareTTM, operatingCashFlowSalesRatioTTM, freeCashFlowOperatingCashFlowRatioTTM, cashFlowCoverageRatiosTTM, shortTermCoverageRatiosTTM, capitalExpenditureCoverageRatioTTM, dividendPaidAndCapexCoverageRatioTTM, priceBookValueRatioTTM, priceToBookRatioTTM, priceToSalesRatioTTM, priceEarningsRatioTTM, priceToFreeCashFlowsRatioTTM, priceToOperatingCashFlowsRatioTTM, priceCashFlowRatioTTM, priceEarningsToGrowthRatioTTM, priceSalesRatioTTM, dividendYieldTTM, enterpriseValueMultipleTTM, priceFairValueTTM, dividendPerShareTTM, bookValuePerShareTTM, tangibleBookValuePerShareTTM, shareholdersEquityPerShareTTM, interestDebtPerShareTTM, marketCapTTM, enterpriseValueTTM, evToSalesTTM, evToOperatingCashFlowTTM, evToFreeCashFlowTTM, earningsYieldTTM, freeCashFlowYieldTTM

Other datasets (key fields)

• income_statement: revenue, grossProfit, operatingIncome, netIncome, eps, ebitda, ...
• balance_sheet: totalAssets, totalLiabilities, totalEquity, cashAndCashEquivalents, totalDebt, ...
• cash_flow: operatingCashFlow, capitalExpenditure, freeCashFlow, dividendsPaid, ...
• earnings: date, eps, epsEstimated, revenue, revenueEstimated, ...
• profile: companyName, sector, industry, country, exchange, ...

sector

sector(symbol: str) -> str | None

Returns: GICS sector name string, or None.

Example (synthetic data)

ctx.data.sector("SYN_001")  # "Technology"

market_cap

market_cap(symbol: str) -> float | None

Returns: Market capitalization in USD (float). Used internally; not exposed via public APIs.

Example (synthetic data)

ctx.data.market_cap("SYN_001")  # 250000000000.0

Common Recipes

returns = ctx.data.returns(lookback=126)
cum = (1 + returns).prod() - 1
top = cum.sort_values(ascending=False).head(20)
w = 1.0 / len(top)
signal = {sym: round(w, 6) for sym in top.index}

all_syms = list(ctx.data.returns(lookback=5).columns)
return [s for s in all_syms if ctx.data.sector(s) == "Technology"]

scores = {}
for sym in universe:
    m = ctx.data.fundamentals(sym, "key_metrics_ttm")
    if m and m.get("returnOnEquityTTM") and m.get("netProfitMarginTTM"):
        scores[sym] = m["returnOnEquityTTM"] * m["netProfitMarginTTM"]
top = sorted(scores, key=scores.get, reverse=True)[:15]
w = 1.0 / len(top)
signal = {s: w for s in top}

rets = ctx.data.returns(lookback=21)
vol = rets.std()
low_vol = vol.nsmallest(20)
w = 1.0 / len(low_vol)
signal = {sym: round(w, 6) for sym in low_vol.index}

close = ctx.data.close(lookback=63)
z = (close.iloc[-1] - close.mean()) / close.std()
worst = z.nsmallest(20)
w = 1.0 / len(worst)
signal = {sym: round(w, 6) for sym in worst.index}