Model

Data vendor / platform: 20 ways to use Ora

Derived signals and event flags as a product, delivered by webhook to clients. Each example is a complete, runnable request with the real answers it returned (49 requests, captured 2026-10-31), what they mean and, where it fits, the action it would send.

Run withAnswers were captured on ora-fin-0.1.1. Another model changes the request JSON and the Playground link; run it there to see its answers.

Rule functions and facts used by this player (64)

ref 10 sma 8 offset 6 rsi 5 ema 3 avg 3 abs 2 pays_dividend 2 adx 2 atr_pct 2 bblower 2 bbmid 2 days_since_split 2 log 2 sqrt 2 std 2 obv 2 atr 2 beat_eps_last 1 beat_revenue_last 1 beat_streak 1 eps_growth_yoy 1 eps_surprise_pct 1 last_earnings_move 1 cut_last 1 days_to_ex_div 1 div_yield_ttm 1 raised_last 1 years_of_growth 1 doji 1 engulfing_bear 1 engulfing_bull 1 evening_star 1 hammer 1 marubozu 1 morning_star 1 shooting_star 1 bbupper 1 macdhist 1 days_to_split 1 last_split_ratio 1 reverse_split_last 1 median 1 percentrank 1 swinghigh 1 swinglow 1 macd 1 macdsignal 1 stochd 1 stochk 1 diminus 1 diplus 1 linreg 1 highest 1 zscore 1 days_to_earnings 1 greatest 1 hl2 1 hlc3 1 least 1 ohlc4 1 tr 1 rec 1 rma 1

Examples 11–20 of 20 · page 2 of 2

Data vendor / platform · example 11

QA: diff an in-house indicator feed against Ora for JPM

Daily QA job: compute a handful of indicators in-house and compare with Ora's exact rules; email on any mismatch.

ora-fin-0.1.1JPM5 questions1 action

StateJPM
Questions5 asked
Answersreal, captured
Actions1 rule · dry run

State

{"symbol":"JPM"} The server fills in the latest completed session.

Questions

`rsi`	English → rule	Is the 14-day RSI above 50?
`macd`	English → rule	Is the MACD line above its signal line?
`bb`	English → rule	Is the stock above the middle Bollinger Band?
`stoch`	English → rule	Is stochastic %K above %D?
`obv`	English → rule	Is on-balance volume higher than 20 sessions ago?

Answers

Question	Answer	Reading
`rsi` Is the 14-day RSI above 50 compiled to `rsi(C, 14) > 50`	0.4703	on the line (false)
`macd` Is the MACD line above its signal line compiled to `macd(C) > macdsignal(C)`	0.88	true
`bb` Is the stock above the middle Bollinger Band compiled to `C > bbmid(C, 20)`	0.6851	true
`stoch` Is stochastic %K above %D compiled to `stochk(14, 3, 3) > stochd(14, 3, 3)`	0.8036	true
`obv` Is on-balance volume higher than 20 sessions ago compiled to `obv() > ref(obv(), -20)`	0.9091	true

Actions

Rules on the answers decide what to send.

rsi >= 0→Emaildata-qa

What each action would send 1 of 1 fired · dry run, nothing sent

qawould send (dry run)Email → data-qa

{
  "subject": "Indicator QA JPM (2026-10-30)",
  "text": "JPM: RSI over 50 47%, MACD over signal 88%, above mid band 69%, %K over %D 80%, OBV up over 20 sessions 91%. Compare with the in-house feed."
}

What it means

For JPM on the session of 2026-10-30: True now: Is the MACD line above its signal line; Is the stock above the middle Bollinger Band; Is stochastic %K above %D; Is on-balance volume higher than 20 sessions ago. On the line: Is the 14-day RSI above 50. Fired: qa (Email → data-qa).

English questions return the rule they compiled to, so QA can see exactly which parameters the comparison used.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "JPM"
  },
  "questions": {
    "rsi": {
      "type": "yes_no",
      "instructions": "Is the 14-day RSI above 50?"
    },
    "macd": {
      "type": "yes_no",
      "instructions": "Is the MACD line above its signal line?"
    },
    "bb": {
      "type": "yes_no",
      "instructions": "Is the stock above the middle Bollinger Band?"
    },
    "stoch": {
      "type": "yes_no",
      "instructions": "Is stochastic %K above %D?"
    },
    "obv": {
      "type": "yes_no",
      "instructions": "Is on-balance volume higher than 20 sessions ago?"
    }
  },
  "actions": [
    {
      "id": "qa",
      "when": "rsi >= 0",
      "do": "email",
      "connection": "data-qa",
      "subject": "Indicator QA {{symbol}} ({{as_of}})",
      "message": "{{symbol}}: RSI over 50 {{rsi|pct}}, MACD over signal {{macd|pct}}, above mid band {{bb|pct}}, %K over %D {{stoch|pct}}, OBV up over 20 sessions {{obv|pct}}. Compare with the in-house feed."
    }
  ],
  "dry_run": true
}

Response JSON (JPM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "rsi": {
      "yes_no": 0.4703,
      "rule": "rsi(C, 14) > 50",
      "basis": "comparison"
    },
    "macd": {
      "yes_no": 0.88,
      "rule": "macd(C) > macdsignal(C)",
      "basis": "comparison"
    },
    "bb": {
      "yes_no": 0.6851,
      "rule": "C > bbmid(C, 20)",
      "basis": "comparison"
    },
    "stoch": {
      "yes_no": 0.8036,
      "rule": "stochk(14, 3, 3) > stochd(14, 3, 3)",
      "basis": "comparison"
    },
    "obv": {
      "yes_no": 0.9091,
      "rule": "obv() > ref(obv(), -20)",
      "basis": "pattern"
    }
  },
  "context": {
    "symbol": "JPM",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "qa",
      "fired": true,
      "status": "dry_run",
      "do": "email",
      "connection": "data-qa",
      "preview": {
        "subject": "Indicator QA JPM (2026-10-30)",
        "text": "JPM: RSI over 50 47%, MACD over signal 88%, above mid band 69%, %K over %D 80%, OBV up over 20 sessions 91%. Compare with the in-house feed."
      }
    }
  ]
}

bbmid macd macdsignal obv ref rsi stochd stochk

Try it in the Playground

Data vendor / platform · example 12

A trend-strength score input for energy names

Score trend strength from ADX, directional movement and stacked averages for an energy screen product.

ora-fin-0.1.13 symbols, one request each4 questions

State3 symbols
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"XOM"} then CVX, COP, one request each. The server fills in the latest completed session.

Questions

`adx`	rule	`adx(14) > 25` Trending (ADX above 25)
`di`	rule	`diplus(14) > diminus(14)` +DI above -DI: buyers in control
`stacked`	rule	`C > ema(C, 20) and ema(C, 20) > sma(C, 50) and sma(C, 50) > sma(C, 200)` Stacked averages: close > 20 EMA > 50 > 200
`slope`	rule	`linreg(C, 50) > ref(linreg(C, 50), -10)` 50-day regression line higher than two weeks ago

Answers

Symbol	`adx`rule	`di`rule	`stacked`rule	`slope`rule
`XOM`	0.8859	0.9409	0.9436	0.9329
`CVX`	0.9102	0.9374	0.9402	0.9276
`COP`	0.9295	0.9317	0.9328	0.9079

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 3 symbols: Trending (ADX above 25): true for XOM, CVX, COP of 3. +DI above -DI: buyers in control: true for XOM, CVX, COP of 3. Stacked averages: close > 20 EMA > 50 > 200: true for XOM, CVX, COP of 3. 50-day regression line higher than two weeks ago: true for XOM, CVX, COP of 3.

The score is the count of true rules; clients get both the score and the answers so they can reweight.

Request JSON (XOM; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "XOM"
  },
  "questions": {
    "adx": {
      "type": "yes_no",
      "ask": "adx(14) > 25"
    },
    "di": {
      "type": "yes_no",
      "ask": "diplus(14) > diminus(14)"
    },
    "stacked": {
      "type": "yes_no",
      "ask": "C > ema(C, 20) and ema(C, 20) > sma(C, 50) and sma(C, 50) > sma(C, 200)"
    },
    "slope": {
      "type": "yes_no",
      "ask": "linreg(C, 50) > ref(linreg(C, 50), -10)"
    }
  }
}

Response JSON (XOM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "adx": {
      "yes_no": 0.8859,
      "rule": "adx(14) > 25",
      "basis": "rule"
    },
    "di": {
      "yes_no": 0.9409,
      "rule": "diplus(14) > diminus(14)",
      "basis": "rule"
    },
    "stacked": {
      "yes_no": 0.9436,
      "rule": "C > ema(C, 20) and ema(C, 20) > sma(C, 50) and sma(C, 50) > sma(C, 200)",
      "basis": "rule"
    },
    "slope": {
      "yes_no": 0.9329,
      "rule": "linreg(C, 50) > ref(linreg(C, 50), -10)",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "XOM",
    "as_of": "2026-10-30"
  }
}

adx diminus diplus ema linreg ref sma

Try it in the Playground

Data vendor / platform · example 13

Flow proxy for bond ETFs: OBV and volume trends in AGG, LQD, HYG

Use volume-based rules as a rough proxy for flows into bond funds.

ora-fin-0.1.13 symbols, one request each4 questions

State3 symbols
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"AGG"} then LQD, HYG, one request each. The server fills in the latest completed session.

Questions

`obv`	rule	`obv() > sma(obv(), 20)` On-balance volume above its 20-day average
`vol`	rule	`V > avg(V, 20)` Volume above its 20-day average
`up_vol`	English → rule	Did the stock rise on above-average volume?
`trend`	rule	`C > sma(C, 50)` Above the 50-day average

Answers

Symbol	`obv`rule	`vol`rule	`up_vol`rule	`trend`rule
`AGG`	0.9099	0.9329	0.81	0.8598
`LQD`	0.9005	0.8408	0.5789	0.6552
`HYG`	0.1675	0.7113	0.1673	0.1914

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 3 symbols: On-balance volume above its 20-day average: true for AGG, LQD of 3. Volume above its 20-day average: true for AGG, LQD, HYG of 3. Did the stock rise on above-average volume: true for AGG of 3. Above the 50-day average: true for AGG, LQD of 3.

Volume rules are a proxy, not real flow data; the product documentation says so plainly.

Request JSON (AGG; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "AGG"
  },
  "questions": {
    "obv": {
      "type": "yes_no",
      "ask": "obv() > sma(obv(), 20)"
    },
    "vol": {
      "type": "yes_no",
      "ask": "V > avg(V, 20)"
    },
    "up_vol": {
      "type": "yes_no",
      "instructions": "Did the stock rise on above-average volume?"
    },
    "trend": {
      "type": "yes_no",
      "ask": "C > sma(C, 50)"
    }
  }
}

Response JSON (AGG, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "obv": {
      "yes_no": 0.9099,
      "rule": "obv() > sma(obv(), 20)",
      "basis": "rule"
    },
    "vol": {
      "yes_no": 0.9329,
      "rule": "V > avg(V, 20)",
      "basis": "rule"
    },
    "up_vol": {
      "yes_no": 0.81,
      "rule": "(C > C[-1]) and (V > ref(avg(V, 20), -1))",
      "basis": "pattern"
    },
    "trend": {
      "yes_no": 0.8598,
      "rule": "C > sma(C, 50)",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "AGG",
    "as_of": "2026-10-30"
  }
}

avg obv offset ref sma

Try it in the Playground

Data vendor / platform · example 14

Client Slack alerts for 52-week highs in mega caps

Post to a client's Slack channel when one of their tracked names makes a new 52-week closing high.

ora-fin-0.1.12 symbols, one request each4 questions1 action

State2 symbols
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

{"symbol":"MSFT"} then COST, one request each. The server fills in the latest completed session.

Questions

`high`	English → rule	Did the stock make a new 52-week closing high?
`vol`	rule	`V > avg(V, 20)` Volume above its 20-day average
`ext`	English → rule	Is the stock more than 10% above its 200-day moving average?
`drop5`	forecast	Will it drop at least 5% at some point in the next month? A 5% drawdown within a month

Answers

Symbol	`high`rule	`vol`rule	`ext`rule	`drop5`forecast	Fired
`MSFT`	0.1038	0.9105	0.9431	0.422	none
`COST`	0.0614	0.9297	0.0813	0.319	none

true false not known for rules; forecasts are odds. Hover a column for its question.

Actions

Rules on the answers decide what to send. Each symbol runs its own request, so each one can fire.

high >= 0.6→Slackclient-acme-alerts

MSFT 0 of 1 fired · dry run, nothing sent

COST 0 of 1 fired · dry run, nothing sent

What it means

Session of 2026-10-30, 2 symbols: Did the stock make a new 52-week closing high: true for none of 2. Volume above its 20-day average: true for MSFT, COST of 2. Is the stock more than 10% above its 200-day moving average: true for MSFT of 2. Highest odds for “A 5% drawdown within a month”: MSFT at 42%. client_slack fired for none.

A client channel only hears about real highs; the extension flag adds context without a forecast.

Request JSON (MSFT; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "MSFT"
  },
  "questions": {
    "high": {
      "type": "yes_no",
      "instructions": "Did the stock make a new 52-week closing high?"
    },
    "vol": {
      "type": "yes_no",
      "ask": "V > avg(V, 20)"
    },
    "ext": {
      "type": "yes_no",
      "instructions": "Is the stock more than 10% above its 200-day moving average?"
    },
    "drop5": {
      "type": "yes_no",
      "instructions": "Will it drop at least 5% at some point in the next month?"
    }
  },
  "actions": [
    {
      "id": "client_slack",
      "when": "high >= 0.6",
      "do": "slack",
      "connection": "client-acme-alerts",
      "message": "{{symbol}} ({{as_of}}) closed at a new 52-week high. Above-average volume {{vol|pct}}; more than 10% over its 200-day {{ext|pct}}."
    }
  ],
  "dry_run": true
}

Response JSON (MSFT, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "high": {
      "yes_no": 0.1038,
      "rule": "C > highest(C, 252)",
      "basis": "pattern"
    },
    "vol": {
      "yes_no": 0.9105,
      "rule": "V > avg(V, 20)",
      "basis": "rule"
    },
    "ext": {
      "yes_no": 0.9431,
      "rule": "C > sma(C, 200) * 1.1",
      "basis": "comparison"
    },
    "drop5": {
      "yes_no": 0.422
    }
  },
  "context": {
    "symbol": "MSFT",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "client_slack",
      "fired": false,
      "status": "skipped"
    }
  ]
}

avg highest sma

Try it in the Playground

Data vendor / platform · example 15

Mean-reversion signal dataset for consumer names

Deliver oversold signals with their trend context for a mean-reversion client.

ora-fin-0.1.13 symbols, one request each5 questions

State3 symbols
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"NKE"} then SBUX, MCD, one request each. The server fills in the latest completed session.

Questions

`rsi2`	rule	`rsi(C, 2) < 10` Two-day RSI under 10
`z`	rule	`zscore(C, 20) < -2` Two deviations under the 20-day mean
`band`	rule	`C < bblower(C, 20, 2)` Closed under the lower Bollinger band
`trend`	English → rule	Is the stock above its 200-day moving average?
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Symbol	`rsi2`rule	`z`rule	`band`rule	`trend`rule	`up_week`forecast
`NKE`	0.0575	0.0561	0.0537	0.0537	0.598
`SBUX`	0.1419	0.0657	0.0537	0.8058	0.504
`MCD`	0.0575	0.056	0.0537	0.0657	0.522

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 3 symbols: Two-day RSI under 10: true for none of 3. Two deviations under the 20-day mean: true for none of 3. Closed under the lower Bollinger band: true for none of 3. Is the stock above its 200-day moving average: true for SBUX of 3. Highest odds for “Higher in a week”: NKE at 60%.

Oversold rules with the trend flag let clients choose between buying dips in uptrends only or in any trend.

Request JSON (NKE; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "NKE"
  },
  "questions": {
    "rsi2": {
      "type": "yes_no",
      "ask": "rsi(C, 2) < 10"
    },
    "z": {
      "type": "yes_no",
      "ask": "zscore(C, 20) < -2"
    },
    "band": {
      "type": "yes_no",
      "ask": "C < bblower(C, 20, 2)"
    },
    "trend": {
      "type": "yes_no",
      "instructions": "Is the stock above its 200-day moving average?"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  }
}

Response JSON (NKE, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "rsi2": {
      "yes_no": 0.0575,
      "rule": "rsi(C, 2) < 10",
      "basis": "rule"
    },
    "z": {
      "yes_no": 0.0561,
      "rule": "zscore(C, 20) < -2",
      "basis": "rule"
    },
    "band": {
      "yes_no": 0.0537,
      "rule": "C < bblower(C, 20, 2)",
      "basis": "rule"
    },
    "trend": {
      "yes_no": 0.0537,
      "rule": "C > sma(C, 200)",
      "basis": "comparison"
    },
    "up_week": {
      "yes_no": 0.598
    }
  },
  "context": {
    "symbol": "NKE",
    "as_of": "2026-10-30"
  }
}

bblower rsi sma zscore

Try it in the Playground

Data vendor / platform · example 16

Realized volatility term proxy for GLD, SLV and USO

Ship short and long realized-volatility states for commodity funds, with the forecast for the next month.

ora-fin-0.1.13 symbols, one request each4 questions

State3 symbols
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"GLD"} then SLV, USO, one request each. The server fills in the latest completed session.

Questions

`short_gt_long`	English → rule	Is 5-day volatility higher than 20-day volatility?
`hv`	rule	`std(log(C / C[-1]), 60) * sqrt(252) > 0.25` 60-day realized volatility above 25% a year
`atr_up`	rule	`atr(14) > 1.2 * ref(atr(14), -10)` ATR up 20% in two weeks
`hv_next`	forecast	Will annualized volatility over the next 21 sessions exceed 30%? Next-month volatility above 30%

Answers

Symbol	`short_gt_long`rule	`hv`rule	`atr_up`rule	`hv_next`forecast
`GLD`	0.9045	0.1074	0.0733	0.567
`SLV`	0.7039	0.9463	0.0583	0.439
`USO`	0.4049	0.946	0.0961	0.586

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 3 symbols: Is 5-day volatility higher than 20-day volatility: true for GLD, SLV of 3. 60-day realized volatility above 25% a year: true for SLV, USO of 3. ATR up 20% in two weeks: true for none of 3. Highest odds for “Next-month volatility above 30%”: USO at 59%.

Short-over-long volatility describes a rising regime; the forecast field is labeled as a probability in the schema.

Request JSON (GLD; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "GLD"
  },
  "questions": {
    "short_gt_long": {
      "type": "yes_no",
      "instructions": "Is 5-day volatility higher than 20-day volatility?"
    },
    "hv": {
      "type": "yes_no",
      "ask": "std(log(C / C[-1]), 60) * sqrt(252) > 0.25"
    },
    "atr_up": {
      "type": "yes_no",
      "ask": "atr(14) > 1.2 * ref(atr(14), -10)"
    },
    "hv_next": {
      "type": "yes_no",
      "instructions": "Will annualized volatility over the next 21 sessions exceed 30%?"
    }
  }
}

Response JSON (GLD, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "short_gt_long": {
      "yes_no": 0.9045,
      "rule": "std(log(C / C[-1]), 5) * sqrt(5 / 4) > std(log(C / C[-1]), 20) * sqrt(20 / 19)",
      "basis": "pattern"
    },
    "hv": {
      "yes_no": 0.1074,
      "rule": "std(log(C / C[-1]), 60) * sqrt(252) > 0.25",
      "basis": "rule"
    },
    "atr_up": {
      "yes_no": 0.0733,
      "rule": "atr(14) > 1.2 * ref(atr(14), -10)",
      "basis": "rule"
    },
    "hv_next": {
      "yes_no": 0.567
    }
  },
  "context": {
    "symbol": "GLD",
    "as_of": "2026-10-30"
  }
}

atr log offset ref sqrt std

Try it in the Playground

Data vendor / platform · example 17

Crypto ETF signals dataset: IBIT, ETHE and MSTR

Add crypto-linked vehicles to the signal feed with the same schema as equities.

ora-fin-0.1.13 symbols, one request each4 questions

State3 symbols
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"IBIT"} then ETHE, MSTR, one request each. The server fills in the latest completed session.

Questions

`trend`	rule	`C > sma(C, 50)` Above the 50-day average
`rsi`	rule	`rsi(C, 14) > 50` RSI above 50
`atr`	rule	`atr_pct(14) > 3` A typical day moves more than 3%
`drop10`	forecast	Will it drop at least 10% at some point in the next month? A 10% drawdown within a month

Answers

Symbol	`trend`rule	`rsi`rule	`atr`rule	`drop10`forecast
`IBIT`	0.1649	0.111	0.2365	0.566
`ETHE`	0.1255	0.1207	0.9437	0.648
`MSTR`	0.1883	0.1221	0.9463	0.777

true false not known for rules; forecasts are odds. Hover a column for its question.

What it means

Session of 2026-10-30, 3 symbols: Above the 50-day average: true for none of 3. RSI above 50: true for none of 3. A typical day moves more than 3%: true for ETHE, MSTR of 3. Highest odds for “A 10% drawdown within a month”: MSTR at 78%.

Same questions, same schema, different asset; clients do not need a separate integration for crypto vehicles.

Request JSON (IBIT; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "IBIT"
  },
  "questions": {
    "trend": {
      "type": "yes_no",
      "ask": "C > sma(C, 50)"
    },
    "rsi": {
      "type": "yes_no",
      "ask": "rsi(C, 14) > 50"
    },
    "atr": {
      "type": "yes_no",
      "ask": "atr_pct(14) > 3"
    },
    "drop10": {
      "type": "yes_no",
      "instructions": "Will it drop at least 10% at some point in the next month?"
    }
  }
}

Response JSON (IBIT, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "trend": {
      "yes_no": 0.1649,
      "rule": "C > sma(C, 50)",
      "basis": "rule"
    },
    "rsi": {
      "yes_no": 0.111,
      "rule": "rsi(C, 14) > 50",
      "basis": "rule"
    },
    "atr": {
      "yes_no": 0.2365,
      "rule": "atr_pct(14) > 3",
      "basis": "rule"
    },
    "drop10": {
      "yes_no": 0.566
    }
  },
  "context": {
    "symbol": "IBIT",
    "as_of": "2026-10-30"
  }
}

atr_pct rsi sma

Try it in the Playground

Data vendor / platform · example 18

Weekly coverage report email to the data team

Each Friday, email the data team a coverage check on flagship symbols, using facts that need the calendar and corporate actions.

ora-fin-0.1.13 symbols, one request each4 questions1 actionworkflow · Fri at 16:50 ET

ScheduleFri at 16:50 ET
State3 symbols
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

{"symbol":"AAPL"} then KO, SPY, one request each. The server fills in the latest completed session.

Questions

`earn_known`	rule	`days_to_earnings < 99999` a next earnings date is on the calendar
`div`	rule	`pays_dividend > 0.5` Pays a dividend
`split_any`	rule	`days_since_split < 99999` any split on record
`rsi`	rule	`rsi(C, 14) > 50` RSI above 50

Answers

Symbol	`earn_known`rule	`div`rule	`split_any`rule	`rsi`rule	Fired
`AAPL`	0.9463	0.8591	0.9463	0.8179	`coverage`
`KO`	0.50	0.8591	0.9463	0.8988	`coverage`
`SPY`	0.50	0.8591	0.45	0.0919	`coverage`

true false not known for rules; forecasts are odds. Hover a column for its question.

Actions

Rules on the answers decide what to send. Each symbol runs its own request, so each one can fire.

rsi >= 0→Emaildata-team

AAPL 1 of 1 fired · dry run, nothing sent

coveragewould send (dry run)Email → data-team

{
  "subject": "Coverage AAPL (2026-10-30)",
  "text": "AAPL: next earnings date known 95%, pays a dividend 86%, split on record 95%."
}

KO 1 of 1 fired · dry run, nothing sent

coveragewould send (dry run)Email → data-team

{
  "subject": "Coverage KO (2026-10-30)",
  "text": "KO: next earnings date known 50%, pays a dividend 86%, split on record 95%."
}

SPY 1 of 1 fired · dry run, nothing sent

coveragewould send (dry run)Email → data-team

{
  "subject": "Coverage SPY (2026-10-30)",
  "text": "SPY: next earnings date known 50%, pays a dividend 86%, split on record 45%."
}

What it means

Session of 2026-10-30, 3 symbols: a next earnings date is on the calendar: true for AAPL of 3. Pays a dividend: true for AAPL, KO, SPY of 3. any split on record: true for AAPL, KO of 3. RSI above 50: true for AAPL, KO of 3. coverage fired for AAPL, KO, SPY.

A fact that answers one half means not known; for an ETF like SPY the earnings check is expected to be unknown, not a gap in data.

As a scheduled workflow (Fri at 16:50 ET)

Create it under Workflows in the console: it runs these questions for every symbol on the schedule and fires the actions per symbol. The answers above are what one run returned.

{
  "name": "Weekly coverage report email to the data team",
  "symbols": "AAPL KO SPY",
  "days": [
    5
  ],
  "at_time": "16:50",
  "model": "ora-fin-0.1.1",
  "questions": {
    "earn_known": {
      "type": "yes_no",
      "ask": "days_to_earnings < 99999"
    },
    "div": {
      "type": "yes_no",
      "ask": "pays_dividend > 0.5"
    },
    "split_any": {
      "type": "yes_no",
      "ask": "days_since_split < 99999"
    },
    "rsi": {
      "type": "yes_no",
      "ask": "rsi(C, 14) > 50"
    }
  },
  "actions": [
    {
      "id": "coverage",
      "when": "rsi >= 0",
      "do": "email",
      "connection": "data-team",
      "subject": "Coverage {{symbol}} ({{as_of}})",
      "message": "{{symbol}}: next earnings date known {{earn_known|pct}}, pays a dividend {{div|pct}}, split on record {{split_any|pct}}."
    }
  ]
}

Request JSON (AAPL; the others differ only in the symbol)

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "AAPL"
  },
  "questions": {
    "earn_known": {
      "type": "yes_no",
      "ask": "days_to_earnings < 99999"
    },
    "div": {
      "type": "yes_no",
      "ask": "pays_dividend > 0.5"
    },
    "split_any": {
      "type": "yes_no",
      "ask": "days_since_split < 99999"
    },
    "rsi": {
      "type": "yes_no",
      "ask": "rsi(C, 14) > 50"
    }
  },
  "actions": [
    {
      "id": "coverage",
      "when": "rsi >= 0",
      "do": "email",
      "connection": "data-team",
      "subject": "Coverage {{symbol}} ({{as_of}})",
      "message": "{{symbol}}: next earnings date known {{earn_known|pct}}, pays a dividend {{div|pct}}, split on record {{split_any|pct}}."
    }
  ],
  "dry_run": true
}

Response JSON (AAPL, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "earn_known": {
      "yes_no": 0.9463,
      "rule": "days_to_earnings < 99999",
      "basis": "rule"
    },
    "div": {
      "yes_no": 0.8591,
      "rule": "pays_dividend > 0.5",
      "basis": "rule"
    },
    "split_any": {
      "yes_no": 0.9463,
      "rule": "days_since_split < 99999",
      "basis": "rule"
    },
    "rsi": {
      "yes_no": 0.8179,
      "rule": "rsi(C, 14) > 50",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "AAPL",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "coverage",
      "fired": true,
      "status": "dry_run",
      "do": "email",
      "connection": "data-team",
      "preview": {
        "subject": "Coverage AAPL (2026-10-30)",
        "text": "AAPL: next earnings date known 95%, pays a dividend 86%, split on record 95%."
      }
    }
  ]
}

days_since_split days_to_earnings pays_dividend rsi

Try it in the Playground

Data vendor / platform · example 19

Bar-shape features for TQQQ: wicks, bodies and true range

Deliver bar-shape features derived from OHLC for clients building their own pattern models.

ora-fin-0.1.1TQQQ5 questions

StateTQQQ
Questions5 asked
Answersreal, captured
Actionsnone

State

{"symbol":"TQQQ"} The server fills in the latest completed session.

Questions

`lower_wick`	rule	`least(O, C) - L > 2 * abs(C - O)` lower wick over twice the body
`upper_wick`	rule	`H - greatest(O, C) > 2 * abs(C - O)` upper wick over twice the body
`wide`	rule	`tr() > 1.5 * ref(atr(14), -1)` true range over 1.5 times yesterday's ATR
`upper_half`	rule	`C > hl2()` closed in the upper half of the range
`typical`	rule	`hlc3() > ohlc4()` typical price above the four-price average

Answers

Question	Answer	Reading
`lower_wick` lower wick over twice the body	0.1612	false
`upper_wick` upper wick over twice the body	0.0804	false
`wide` true range over 1.5 times yesterday's ATR	0.9416	true
`upper_half` closed in the upper half of the range	0.3885	false
`typical` typical price above the four-price average	0.0794	false

What it means

For TQQQ on the session of 2026-10-30: True now: true range over 1.5 times yesterday's ATR. Not true: lower wick over twice the body; upper wick over twice the body; closed in the upper half of the range; typical price above the four-price average.

These are pure arithmetic on the bar, delivered as exact flags; clients combine them as they like.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "TQQQ"
  },
  "questions": {
    "lower_wick": {
      "type": "yes_no",
      "ask": "least(O, C) - L > 2 * abs(C - O)"
    },
    "upper_wick": {
      "type": "yes_no",
      "ask": "H - greatest(O, C) > 2 * abs(C - O)"
    },
    "wide": {
      "type": "yes_no",
      "ask": "tr() > 1.5 * ref(atr(14), -1)"
    },
    "upper_half": {
      "type": "yes_no",
      "ask": "C > hl2()"
    },
    "typical": {
      "type": "yes_no",
      "ask": "hlc3() > ohlc4()"
    }
  }
}

Response JSON (TQQQ, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "lower_wick": {
      "yes_no": 0.1612,
      "rule": "least(O, C) - L > 2 * abs(C - O)",
      "basis": "rule"
    },
    "upper_wick": {
      "yes_no": 0.0804,
      "rule": "H - greatest(O, C) > 2 * abs(C - O)",
      "basis": "rule"
    },
    "wide": {
      "yes_no": 0.9416,
      "rule": "tr() > 1.5 * ref(atr(14), -1)",
      "basis": "rule"
    },
    "upper_half": {
      "yes_no": 0.3885,
      "rule": "C > hl2()",
      "basis": "rule"
    },
    "typical": {
      "yes_no": 0.0794,
      "rule": "hlc3() > ohlc4()",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "TQQQ",
    "as_of": "2026-10-30"
  }
}

abs atr greatest hl2 hlc3 least ohlc4 ref tr

Try it in the Playground

Data vendor / platform · example 20

Recursive filter signal for EEM with rec()

Offer a custom adaptive-filter signal built with rec(), compared with the standard EMA, for an emerging-markets client.

ora-fin-0.1.1EEM4 questions

StateEEM
Questions4 asked
Answersreal, captured
Actionsnone

State

{"symbol":"EEM"} The server fills in the latest completed session.

Questions

`rec`	rule	`C > rec(C, self + 0.2 * (C - self))` close above a recursive filter with alpha one fifth
`ema`	rule	`C > ema(C, 9)` close above the 9-day EMA
`rma`	rule	`rma(C, 14) > ref(rma(C, 14), -5)` 14-day Wilder average rising over a week
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Question	Answer	Reading
`rec` close above a recursive filter with alpha one fifth	0.083	false
`ema` close above the 9-day EMA	0.083	false
`rma` 14-day Wilder average rising over a week	0.081	false
`up_week` Higher in a week	0.55	55% · close to even

What it means

For EEM on the session of 2026-10-30: Not true: close above a recursive filter with alpha one fifth; close above the 9-day EMA; 14-day Wilder average rising over a week. Forecasts: Higher in a week 55%.

A filter with alpha one fifth matches a 9-day EMA; agreement between the two is a built-in check on the custom signal.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "EEM"
  },
  "questions": {
    "rec": {
      "type": "yes_no",
      "ask": "C > rec(C, self + 0.2 * (C - self))"
    },
    "ema": {
      "type": "yes_no",
      "ask": "C > ema(C, 9)"
    },
    "rma": {
      "type": "yes_no",
      "ask": "rma(C, 14) > ref(rma(C, 14), -5)"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  }
}

Response JSON (EEM, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "rec": {
      "yes_no": 0.083,
      "rule": "C > rec(C, self + 0.2 * (C - self))",
      "basis": "rule"
    },
    "ema": {
      "yes_no": 0.083,
      "rule": "C > ema(C, 9)",
      "basis": "rule"
    },
    "rma": {
      "yes_no": 0.081,
      "rule": "rma(C, 14) > ref(rma(C, 14), -5)",
      "basis": "rule"
    },
    "up_week": {
      "yes_no": 0.55
    }
  },
  "context": {
    "symbol": "EEM",
    "as_of": "2026-10-30"
  }
}

ema rec ref rma

Try it in the Playground

Information, not investment advice. Rules answer from the latest completed daily session; forecasts are probabilities. Actions ran as dry runs: nothing was sent. More in Builders: Quant developer, Fintech app builder, Robo-advisor, Broker app, Algo / bot developer, Trading educator / newsletter.

Create an account Open the Playground Everything here also lives in the console, next to your keys and usage.

Individuals

Buy side

Sell side

Corporates

Builders

Institutions

QA: diff an in-house indicator feed against Ora for JPM

State

Questions

Answers

Actions

What it means

A trend-strength score input for energy names

State

Questions

Answers

What it means

Flow proxy for bond ETFs: OBV and volume trends in AGG, LQD, HYG

State

Questions

Answers

What it means

Client Slack alerts for 52-week highs in mega caps

State

Questions

Answers

Actions

What it means

Mean-reversion signal dataset for consumer names

State

Questions

Answers

What it means

Realized volatility term proxy for GLD, SLV and USO

State

Questions

Answers

What it means

Crypto ETF signals dataset: IBIT, ETHE and MSTR

State

Questions

Answers

What it means

Weekly coverage report email to the data team

State

Questions

Answers

Actions

What it means

Bar-shape features for TQQQ: wicks, bodies and true range

State

Questions

Answers

What it means

Recursive filter signal for EEM with rec()

State

Questions

Answers

What it means