Model

Quant developer: 20 ways to use Ora

Wires exact rules and forecasts into pipelines, tests and order systems. Each example is a complete, runnable request with the real answers it returned (32 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)

sma 8 ref 7 offset 6 avg 5 ema 3 abs 3 atr 3 crossup 2 wma 2 adx 2 highest 2 log 2 sqrt 2 std 2 zscore 2 rsi 2 sum 2 mean 2 crossdown 1 diminus 1 diplus 1 linreg 1 median 1 gap_down 1 gap_up 1 inside_bar 1 marubozu 1 outside_bar 1 spinning_top 1 cum 1 rec 1 rma 1 macd 1 macdsignal 1 cci 1 stochd 1 stochk 1 willr 1 days_since_earnings 1 days_to_earnings 1 last_earnings_move 1 next_report_amc 1 barssince 1 highestbars 1 lowestbars 1 bblower 1 between 1 iif 1 sign 1 greatest 1 hammer 1 hanging_man 1 inverted_hammer 1 least 1 hl2 1 hlc3 1 ohlc4 1 tr 1 swinghigh 1 swinglow 1 lowest 1 max 1 obv 1 percentrank 1

Examples 1–10 of 20 · page 1 of 2

Quant developer · example 1

Unit-test a moving-average signal on SPY against the rule engine

Before shipping a crossover signal in a pipeline, compare the in-house computation with Ora's exact rules for the same session.

ora-fin-0.1.1SPY4 questions1 action

StateSPY
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

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

Questions

`cross_50`	rule	`crossup(C, sma(C, 50))` Crossed above the 50-day today
`ema_wma`	rule	`wma(C, 10) > ema(C, 30)` 10-day weighted average above the 30-day EMA
`sma50_up`	rule	`sma(C, 50) > ref(sma(C, 50), -10)` The 50-day average is rising
`dn_cross`	rule	`crossdown(sma(C, 20), sma(C, 50))` 20-day average crossed under the 50-day today

Answers

Question	Answer	Reading
`cross_50` Crossed above the 50-day today	0.05	false
`ema_wma` 10-day weighted average above the 30-day EMA	0.3059	false
`sma50_up` The 50-day average is rising	0.2481	false
`dn_cross` 20-day average crossed under the 50-day today	0.05	false

Actions

Rules on the answers decide what to send.

cross_50 >= 0→Webhookci-results

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

test_resultwould send (dry run)Webhook → ci-results

{
  "suite": "signal_parity",
  "symbol": "SPY",
  "as_of": "2026-10-30",
  "cross_50d": 0.05,
  "wma_over_ema": 0.3059,
  "sma50_rising": 0.2481,
  "cross_20_50_down": 0.05
}

What it means

For SPY on the session of 2026-10-30: Not true: Crossed above the 50-day today; 10-day weighted average above the 30-day EMA; The 50-day average is rising; 20-day average crossed under the 50-day today. Fired: test_result (Webhook → ci-results).

Each answer carries the rule that was evaluated, so a test can assert the in-house flag matches the rule's yes or no for the same as_of date.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "SPY"
  },
  "questions": {
    "cross_50": {
      "type": "yes_no",
      "ask": "crossup(C, sma(C, 50))"
    },
    "ema_wma": {
      "type": "yes_no",
      "ask": "wma(C, 10) > ema(C, 30)"
    },
    "sma50_up": {
      "type": "yes_no",
      "ask": "sma(C, 50) > ref(sma(C, 50), -10)"
    },
    "dn_cross": {
      "type": "yes_no",
      "ask": "crossdown(sma(C, 20), sma(C, 50))"
    }
  },
  "actions": [
    {
      "id": "test_result",
      "when": "cross_50 >= 0",
      "do": "webhook",
      "connection": "ci-results",
      "body": {
        "suite": "signal_parity",
        "symbol": "{{symbol}}",
        "as_of": "{{as_of}}",
        "cross_50d": "{{cross_50}}",
        "wma_over_ema": "{{ema_wma}}",
        "sma50_rising": "{{sma50_up}}",
        "cross_20_50_down": "{{dn_cross}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (SPY, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "cross_50": {
      "yes_no": 0.05,
      "rule": "crossup(C, sma(C, 50))",
      "basis": "rule"
    },
    "ema_wma": {
      "yes_no": 0.3059,
      "rule": "wma(C, 10) > ema(C, 30)",
      "basis": "rule"
    },
    "sma50_up": {
      "yes_no": 0.2481,
      "rule": "sma(C, 50) > ref(sma(C, 50), -10)",
      "basis": "rule"
    },
    "dn_cross": {
      "yes_no": 0.05,
      "rule": "crossdown(sma(C, 20), sma(C, 50))",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "SPY",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "test_result",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "ci-results",
      "preview": {
        "body": {
          "suite": "signal_parity",
          "symbol": "SPY",
          "as_of": "2026-10-30",
          "cross_50d": 0.05,
          "wma_over_ema": 0.3059,
          "sma50_rising": 0.2481,
          "cross_20_50_down": 0.05
        }
      }
    }
  ]
}

crossdown crossup ema ref sma wma

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Quant developer · example 2

Send a QQQ breakout signal to the order service as typed JSON

A breakout rule plus a volume rule plus the one-week forecast decide whether a signed webhook carries an entry signal to the order management service.

ora-fin-0.1.1QQQ4 questions2 actions

StateQQQ
Questions4 asked
Answersreal, captured
Actions2 rules · dry run

State

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

Questions

`breakout`	rule	`C > highest(C, 20)` 20-day closing breakout
`volume`	rule	`V > 2 * ref(avg(V, 20), -1)` Volume more than twice the previous 20-day average
`adx`	rule	`adx(14) > 25` Trending (ADX above 25)
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Question	Answer	Reading
`breakout` 20-day closing breakout	0.0537	false
`volume` Volume more than twice the previous 20-day average	0.766	true
`adx` Trending (ADX above 25)	0.6956	true
`up_week` Higher in a week	0.561	56% · close to even

Actions

Rules on the answers decide what to send.

breakout >= 0.6 and volume >= 0.6 and up_week >= 0.55→Webhookoms-signals

else→Webhookoms-signals

What each action would send 1 of 2 fired · dry run, nothing sent

entryrule false: skipped

no_signalwould send (dry run)Webhook → oms-signals

{
  "symbol": "QQQ",
  "signal": "none",
  "p_breakout": 0.0537,
  "as_of": "2026-10-30"
}

What it means

For QQQ on the session of 2026-10-30: True now: Volume more than twice the previous 20-day average; Trending (ADX above 25). Not true: 20-day closing breakout. Forecasts: Higher in a week 56%. Fired: no_signal (Webhook → oms-signals).

The order service receives exactly one body per run, either an entry or an explicit none, so downstream code never has to infer silence.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "QQQ"
  },
  "questions": {
    "breakout": {
      "type": "yes_no",
      "ask": "C > highest(C, 20)"
    },
    "volume": {
      "type": "yes_no",
      "ask": "V > 2 * ref(avg(V, 20), -1)"
    },
    "adx": {
      "type": "yes_no",
      "ask": "adx(14) > 25"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  },
  "actions": [
    {
      "id": "entry",
      "when": "breakout >= 0.6 and volume >= 0.6 and up_week >= 0.55",
      "do": "webhook",
      "connection": "oms-signals",
      "body": {
        "symbol": "{{symbol}}",
        "signal": "breakout_long",
        "p_breakout": "{{breakout}}",
        "p_volume": "{{volume}}",
        "p_trend": "{{adx}}",
        "p_up_week": "{{up_week}}",
        "as_of": "{{as_of}}"
      }
    },
    {
      "id": "no_signal",
      "when": "else",
      "do": "webhook",
      "connection": "oms-signals",
      "body": {
        "symbol": "{{symbol}}",
        "signal": "none",
        "p_breakout": "{{breakout}}",
        "as_of": "{{as_of}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (QQQ, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "breakout": {
      "yes_no": 0.0537,
      "rule": "C > highest(C, 20)",
      "basis": "rule"
    },
    "volume": {
      "yes_no": 0.766,
      "rule": "V > 2 * ref(avg(V, 20), -1)",
      "basis": "rule"
    },
    "adx": {
      "yes_no": 0.6956,
      "rule": "adx(14) > 25",
      "basis": "rule"
    },
    "up_week": {
      "yes_no": 0.561
    }
  },
  "context": {
    "symbol": "QQQ",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "entry",
      "fired": false,
      "status": "skipped"
    },
    {
      "id": "no_signal",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "oms-signals",
      "preview": {
        "body": {
          "symbol": "QQQ",
          "signal": "none",
          "p_breakout": 0.0537,
          "as_of": "2026-10-30"
        }
      }
    }
  ]
}

adx avg highest ref

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Quant developer · example 3

Build a regime feature vector for AAPL from exact rules

Turn trend, volatility and momentum states into binary features for a model, each computed by the rule engine rather than reimplemented.

ora-fin-0.1.1AAPL6 questions1 action

StateAAPL
Questions6 asked
Answersreal, captured
Actions1 rule · dry run

State

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

Questions

`trend`	rule	`adx(14) > 20 and diplus(14) > diminus(14)` Trending up (ADX over 20 and +DI over −DI)
`linreg_up`	rule	`linreg(C, 20) > ref(linreg(C, 20), -5)` 20-day regression line higher than a week ago
`vol_regime`	rule	`std(log(C / C[-1]), 20) * sqrt(252) > 0.4` Annualized 20-day volatility above 40%
`zs`	rule	`zscore(C, 50) > 1` close more than 1 standard deviation above its 50-day mean
`median_vol`	rule	`V > 2 * median(V, 20)` volume over twice its 20-day median
`drop5`	forecast	Will it drop at least 5% at some point in the next month? A 5% drawdown within a month

Answers

Question	Answer	Reading
`trend` Trending up (ADX over 20 and +DI over −DI)	0.2895	false
`linreg_up` 20-day regression line higher than a week ago	0.6301	true
`vol_regime` Annualized 20-day volatility above 40%	0.0537	false
`zs` close more than 1 standard deviation above its 50-day mean	0.5609	on the line (true)
`median_vol` volume over twice its 20-day median	0.2151	false
`drop5` A 5% drawdown within a month	0.365	37% · unlikely

Actions

Rules on the answers decide what to send.

trend >= 0→Webhookfeature-store

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

featureswould send (dry run)Webhook → feature-store

{
  "entity": "AAPL",
  "as_of": "2026-10-30",
  "adx_uptrend": 0.2895,
  "linreg_up": 0.6301,
  "hv_over_40": 0.0537,
  "zscore_over_1": 0.5609,
  "volume_over_2x_median": 0.2151,
  "p_drop5": 0.365
}

What it means

For AAPL on the session of 2026-10-30: True now: 20-day regression line higher than a week ago. Not true: Trending up (ADX over 20 and +DI over −DI); Annualized 20-day volatility above 40%; volume over twice its 20-day median. On the line: close more than 1 standard deviation above its 50-day mean. Forecasts: A 5% drawdown within a month 37%. Fired: features (Webhook → feature-store).

Rule answers near one or zero become clean binary features; the forecast column stays a probability and is stored as such.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "AAPL"
  },
  "questions": {
    "trend": {
      "type": "yes_no",
      "ask": "adx(14) > 20 and diplus(14) > diminus(14)"
    },
    "linreg_up": {
      "type": "yes_no",
      "ask": "linreg(C, 20) > ref(linreg(C, 20), -5)"
    },
    "vol_regime": {
      "type": "yes_no",
      "ask": "std(log(C / C[-1]), 20) * sqrt(252) > 0.4"
    },
    "zs": {
      "type": "yes_no",
      "ask": "zscore(C, 50) > 1"
    },
    "median_vol": {
      "type": "yes_no",
      "ask": "V > 2 * median(V, 20)"
    },
    "drop5": {
      "type": "yes_no",
      "instructions": "Will it drop at least 5% at some point in the next month?"
    }
  },
  "actions": [
    {
      "id": "features",
      "when": "trend >= 0",
      "do": "webhook",
      "connection": "feature-store",
      "body": {
        "entity": "{{symbol}}",
        "as_of": "{{as_of}}",
        "adx_uptrend": "{{trend}}",
        "linreg_up": "{{linreg_up}}",
        "hv_over_40": "{{vol_regime}}",
        "zscore_over_1": "{{zs}}",
        "volume_over_2x_median": "{{median_vol}}",
        "p_drop5": "{{drop5}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (AAPL, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "trend": {
      "yes_no": 0.2895,
      "rule": "adx(14) > 20 and diplus(14) > diminus(14)",
      "basis": "rule"
    },
    "linreg_up": {
      "yes_no": 0.6301,
      "rule": "linreg(C, 20) > ref(linreg(C, 20), -5)",
      "basis": "rule"
    },
    "vol_regime": {
      "yes_no": 0.0537,
      "rule": "std(log(C / C[-1]), 20) * sqrt(252) > 0.4",
      "basis": "rule"
    },
    "zs": {
      "yes_no": 0.5609,
      "rule": "zscore(C, 50) > 1",
      "basis": "rule"
    },
    "median_vol": {
      "yes_no": 0.2151,
      "rule": "V > 2 * median(V, 20)",
      "basis": "rule"
    },
    "drop5": {
      "yes_no": 0.365
    }
  },
  "context": {
    "symbol": "AAPL",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "features",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "feature-store",
      "preview": {
        "body": {
          "entity": "AAPL",
          "as_of": "2026-10-30",
          "adx_uptrend": 0.2895,
          "linreg_up": 0.6301,
          "hv_over_40": 0.0537,
          "zscore_over_1": 0.5609,
          "volume_over_2x_median": 0.2151,
          "p_drop5": 0.365
        }
      }
    }
  ]
}

adx diminus diplus linreg log median offset ref sqrt std zscore

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Quant developer · example 4

Scan a five-name tech universe for oversold-in-uptrend setups

Run the same rule set over a small universe in parallel and rank by the drawdown forecast.

ora-fin-0.1.15 symbols, one request each4 questions1 action

State5 symbols
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

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

Questions

`setup`	rule	`(C > sma(C, 200) and rsi(C, 14) < 40) or (C < sma(C, 200) and rsi(C, 14) < 25)` Oversold, with the line set by the trend
`rsi2`	rule	`rsi(C, 2) < 10` Two-day RSI under 10
`above_200`	rule	`C > sma(C, 200) * 1.10` More than 10% above the 200-day
`drop5`	forecast	Will it drop at least 5% at some point in the next month? A 5% drawdown within a month

Answers

Symbol	`setup`rule	`rsi2`rule	`above_200`rule	`drop5`forecast	Fired
`AAPL`	0.0634	0.1464	0.934	0.365	none
`MSFT`	0.1064	0.5812	0.9431	0.422	none
`NVDA`	0.5455	0.591	0.06	0.523	none
`AMD`	0.5217	0.4518	0.8701	0.763	none
`ORCL`	0.0537	0.2388	0.0537	0.653	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.

setup >= 0.6 and drop5 < 0.5→Slackquant-dev

AAPL 0 of 1 fired · dry run, nothing sent

MSFT 0 of 1 fired · dry run, nothing sent

NVDA 0 of 1 fired · dry run, nothing sent

AMD 0 of 1 fired · dry run, nothing sent

ORCL 0 of 1 fired · dry run, nothing sent

What it means

Session of 2026-10-30, 5 symbols: Oversold, with the line set by the trend: true for none of 5. Two-day RSI under 10: true for none of 5. More than 10% above the 200-day: true for AAPL, MSFT, AMD of 5. Highest odds for “A 5% drawdown within a month”: AMD at 76%. candidate fired for none.

One request per symbol with identical keys makes the results a table; filter on the setup rule and sort by the drawdown odds.

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

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "AAPL"
  },
  "questions": {
    "setup": {
      "type": "yes_no",
      "ask": "(C > sma(C, 200) and rsi(C, 14) < 40) or (C < sma(C, 200) and rsi(C, 14) < 25)"
    },
    "rsi2": {
      "type": "yes_no",
      "ask": "rsi(C, 2) < 10"
    },
    "above_200": {
      "type": "yes_no",
      "ask": "C > sma(C, 200) * 1.10"
    },
    "drop5": {
      "type": "yes_no",
      "instructions": "Will it drop at least 5% at some point in the next month?"
    }
  },
  "actions": [
    {
      "id": "candidate",
      "when": "setup >= 0.6 and drop5 < 0.5",
      "do": "slack",
      "connection": "quant-dev",
      "message": "{{symbol}} ({{as_of}}): oversold-in-regime setup ({{setup|pct}}), RSI(2) {{rsi2|pct}}, 5% drawdown odds {{drop5|pct}}."
    }
  ],
  "dry_run": true
}

Response JSON (AAPL, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "setup": {
      "yes_no": 0.0634,
      "rule": "(C > sma(C, 200) and rsi(C, 14) < 40) or (C < sma(C, 200) and rsi(C, 14) < 25)",
      "basis": "rule"
    },
    "rsi2": {
      "yes_no": 0.1464,
      "rule": "rsi(C, 2) < 10",
      "basis": "rule"
    },
    "above_200": {
      "yes_no": 0.934,
      "rule": "C > sma(C, 200) * 1.10",
      "basis": "rule"
    },
    "drop5": {
      "yes_no": 0.365
    }
  },
  "context": {
    "symbol": "AAPL",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "candidate",
      "fired": false,
      "status": "skipped"
    }
  ]
}

rsi sma

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Quant developer · example 5

Regression-test candlestick detectors on TSLA

Check that a home-grown candlestick detector agrees with Ora's pattern functions on the latest bar.

ora-fin-0.1.1TSLA6 questions

StateTSLA
Questions6 asked
Answersreal, captured
Actionsnone

State

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

Questions

`marubozu`	rule	`marubozu()` marubozu candle: Body at least 90% of the range: almost no wicks.
`spinning`	rule	`spinning_top()` spinning top candle: Small body (10–30% of range) with wicks of at least a quarter of the range on both sides.
`outside`	rule	`outside_bar()` outside bar candle: Today's high above yesterday's and low below it.
`inside`	rule	`inside_bar()` inside bar candle: Today's high and low are inside yesterday's (equal counts).
`gap_up`	rule	`gap_up()` gap up candle: Today's whole range is above yesterday's high: an unfilled gap up.
`gap_down`	rule	`gap_down()` gap down candle: Today's whole range is below yesterday's low.

Answers

Question	Answer	Reading
`marubozu` marubozu candle: Body at least 90% of the range: almost no wicks.	0.0551	false
`spinning` spinning top candle: Small body (10–30% of range) with wicks of at least a quarter of the range on both sides.	0.2832	false
`outside` outside bar candle: Today's high above yesterday's and low below it.	0.3172	false
`inside` inside bar candle: Today's high and low are inside yesterday's (equal counts).	0.0795	false
`gap_up` gap up candle: Today's whole range is above yesterday's high: an unfilled gap up.	0.0577	false
`gap_down` gap down candle: Today's whole range is below yesterday's low.	0.2768	false

What it means

For TSLA on the session of 2026-10-30: Not true: marubozu candle: Body at least 90% of the range: almost no wicks.; spinning top candle: Small body (10–30% of range) with wicks of at least a quarter of the range on both sides.; outside bar candle: Today's high above yesterday's and low below it.; inside bar candle: Today's high and low are inside yesterday's (equal counts).; gap up candle: Today's whole range is above yesterday's high: an unfilled gap up.; gap down candle: Today's whole range is below yesterday's low..

A pattern answer is an exact rule on one bar; disagreement with the in-house detector points at a definition difference worth documenting.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "TSLA"
  },
  "questions": {
    "marubozu": {
      "type": "yes_no",
      "ask": "marubozu()"
    },
    "spinning": {
      "type": "yes_no",
      "ask": "spinning_top()"
    },
    "outside": {
      "type": "yes_no",
      "ask": "outside_bar()"
    },
    "inside": {
      "type": "yes_no",
      "ask": "inside_bar()"
    },
    "gap_up": {
      "type": "yes_no",
      "ask": "gap_up()"
    },
    "gap_down": {
      "type": "yes_no",
      "ask": "gap_down()"
    }
  }
}

Response JSON (TSLA, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "marubozu": {
      "yes_no": 0.0551,
      "rule": "marubozu()",
      "basis": "rule"
    },
    "spinning": {
      "yes_no": 0.2832,
      "rule": "spinning_top()",
      "basis": "rule"
    },
    "outside": {
      "yes_no": 0.3172,
      "rule": "outside_bar()",
      "basis": "rule"
    },
    "inside": {
      "yes_no": 0.0795,
      "rule": "inside_bar()",
      "basis": "rule"
    },
    "gap_up": {
      "yes_no": 0.0577,
      "rule": "gap_up()",
      "basis": "rule"
    },
    "gap_down": {
      "yes_no": 0.2768,
      "rule": "gap_down()",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "TSLA",
    "as_of": "2026-10-30"
  }
}

gap_down gap_up inside_bar marubozu outside_bar spinning_top

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Quant developer · example 6

Compare a custom recursive smoother with built-in averages on MSFT

Prototype an exponential smoother with rec() and check it against ema and rma before porting it to production code.

ora-fin-0.1.1MSFT4 questions

StateMSFT
Questions4 asked
Answersreal, captured
Actionsnone

State

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

Questions

`rec_above`	rule	`C > rec(C, self + 0.1 * (C - self))` close above a recursive smoother with alpha one tenth
`ema_above`	rule	`C > ema(C, 19)` close above the 19-day EMA
`rma_above`	rule	`C > rma(C, 10)` close above the 10-day Wilder average
`cum_up`	rule	`cum(C > C[-1]) > cum(C < C[-1])` more up days than down days in the whole history

Answers

Question	Answer	Reading
`rec_above` close above a recursive smoother with alpha one tenth	0.3041	false
`ema_above` close above the 19-day EMA	0.3041	false
`rma_above` close above the 10-day Wilder average	0.3041	false
`cum_up` more up days than down days in the whole history	0.9463	true

What it means

For MSFT on the session of 2026-10-30: True now: more up days than down days in the whole history. Not true: close above a recursive smoother with alpha one tenth; close above the 19-day EMA; close above the 10-day Wilder average.

A smoother with alpha one tenth should agree with the 19-day EMA most days; a disagreement on this session is a test case to keep.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "MSFT"
  },
  "questions": {
    "rec_above": {
      "type": "yes_no",
      "ask": "C > rec(C, self + 0.1 * (C - self))"
    },
    "ema_above": {
      "type": "yes_no",
      "ask": "C > ema(C, 19)"
    },
    "rma_above": {
      "type": "yes_no",
      "ask": "C > rma(C, 10)"
    },
    "cum_up": {
      "type": "yes_no",
      "ask": "cum(C > C[-1]) > cum(C < C[-1])"
    }
  }
}

Response JSON (MSFT, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "rec_above": {
      "yes_no": 0.3041,
      "rule": "C > rec(C, self + 0.1 * (C - self))",
      "basis": "rule"
    },
    "ema_above": {
      "yes_no": 0.3041,
      "rule": "C > ema(C, 19)",
      "basis": "rule"
    },
    "rma_above": {
      "yes_no": 0.3041,
      "rule": "C > rma(C, 10)",
      "basis": "rule"
    },
    "cum_up": {
      "yes_no": 0.9463,
      "rule": "cum(C > C[-1]) > cum(C < C[-1])",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "MSFT",
    "as_of": "2026-10-30"
  }
}

cum ema offset rec rma

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Quant developer · example 7

Nightly signal snapshot for the index ETFs, posted to a data lake hook

After every close, compute the same four signals for the index ETFs and post each row to an ingestion webhook.

ora-fin-0.1.14 symbols, one request each4 questions1 actionworkflow · Mon–Fri at 16:45 ET

ScheduleMon–Fri at 16:45 ET
State4 symbols
Questions4 asked
Answersreal, captured
Actions1 rule · dry run

State

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

Questions

`trend`	rule	`C > sma(C, 50)` Above the 50-day average
`momentum`	rule	`macd(C) > macdsignal(C)` MACD above its signal line
`breadth_proxy`	rule	`sum(C > C[-1], 10) >= 6` Up on at least six of the last ten days
`up_week`	forecast	Will the stock be higher in a week? Higher in a week

Answers

Symbol	`trend`rule	`momentum`rule	`breadth_proxy`rule	`up_week`forecast	Fired
`SPY`	0.1128	0.1658	0.55	0.598	`row`
`QQQ`	0.0653	0.1238	0.2018	0.561	`row`
`IWM`	0.2176	0.9247	0.9174	0.526	`row`
`DIA`	0.2849	0.6182	0.55	0.567	`row`

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.

trend >= 0→Webhooklake-ingest

SPY 1 of 1 fired · dry run, nothing sent

rowwould send (dry run)Webhook → lake-ingest

{
  "table": "ora_signals",
  "symbol": "SPY",
  "as_of": "2026-10-30",
  "trend": 0.1128,
  "momentum": 0.1658,
  "mostly_up": 0.55,
  "p_up_week": 0.598
}

QQQ 1 of 1 fired · dry run, nothing sent

rowwould send (dry run)Webhook → lake-ingest

{
  "table": "ora_signals",
  "symbol": "QQQ",
  "as_of": "2026-10-30",
  "trend": 0.0653,
  "momentum": 0.1238,
  "mostly_up": 0.2018,
  "p_up_week": 0.561
}

IWM 1 of 1 fired · dry run, nothing sent

rowwould send (dry run)Webhook → lake-ingest

{
  "table": "ora_signals",
  "symbol": "IWM",
  "as_of": "2026-10-30",
  "trend": 0.2176,
  "momentum": 0.9247,
  "mostly_up": 0.9174,
  "p_up_week": 0.526
}

DIA 1 of 1 fired · dry run, nothing sent

rowwould send (dry run)Webhook → lake-ingest

{
  "table": "ora_signals",
  "symbol": "DIA",
  "as_of": "2026-10-30",
  "trend": 0.2849,
  "momentum": 0.6182,
  "mostly_up": 0.55,
  "p_up_week": 0.567
}

What it means

Session of 2026-10-30, 4 symbols: Above the 50-day average: true for none of 4. MACD above its signal line: true for IWM, DIA of 4. Up on at least six of the last ten days: true for IWM of 4. Highest odds for “Higher in a week”: SPY at 60%. row fired for SPY, QQQ, IWM, DIA.

The always-true condition makes every symbol post a row, so the table has one record per symbol per session for later research.

As a scheduled workflow (Mon–Fri at 16:45 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": "Nightly signal snapshot for the index ETFs, posted to a data lake hook",
  "symbols": "SPY QQQ IWM DIA",
  "days": [
    1,
    2,
    3,
    4,
    5
  ],
  "at_time": "16:45",
  "model": "ora-fin-0.1.1",
  "questions": {
    "trend": {
      "type": "yes_no",
      "ask": "C > sma(C, 50)"
    },
    "momentum": {
      "type": "yes_no",
      "ask": "macd(C) > macdsignal(C)"
    },
    "breadth_proxy": {
      "type": "yes_no",
      "ask": "sum(C > C[-1], 10) >= 6"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  },
  "actions": [
    {
      "id": "row",
      "when": "trend >= 0",
      "do": "webhook",
      "connection": "lake-ingest",
      "body": {
        "table": "ora_signals",
        "symbol": "{{symbol}}",
        "as_of": "{{as_of}}",
        "trend": "{{trend}}",
        "momentum": "{{momentum}}",
        "mostly_up": "{{breadth_proxy}}",
        "p_up_week": "{{up_week}}"
      }
    }
  ]
}

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

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "SPY"
  },
  "questions": {
    "trend": {
      "type": "yes_no",
      "ask": "C > sma(C, 50)"
    },
    "momentum": {
      "type": "yes_no",
      "ask": "macd(C) > macdsignal(C)"
    },
    "breadth_proxy": {
      "type": "yes_no",
      "ask": "sum(C > C[-1], 10) >= 6"
    },
    "up_week": {
      "type": "yes_no",
      "instructions": "Will the stock be higher in a week?"
    }
  },
  "actions": [
    {
      "id": "row",
      "when": "trend >= 0",
      "do": "webhook",
      "connection": "lake-ingest",
      "body": {
        "table": "ora_signals",
        "symbol": "{{symbol}}",
        "as_of": "{{as_of}}",
        "trend": "{{trend}}",
        "momentum": "{{momentum}}",
        "mostly_up": "{{breadth_proxy}}",
        "p_up_week": "{{up_week}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (SPY, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "trend": {
      "yes_no": 0.1128,
      "rule": "C > sma(C, 50)",
      "basis": "rule"
    },
    "momentum": {
      "yes_no": 0.1658,
      "rule": "macd(C) > macdsignal(C)",
      "basis": "rule"
    },
    "breadth_proxy": {
      "yes_no": 0.55,
      "rule": "sum(C > C[-1], 10) >= 6",
      "basis": "rule"
    },
    "up_week": {
      "yes_no": 0.598
    }
  },
  "context": {
    "symbol": "SPY",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "row",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "lake-ingest",
      "preview": {
        "body": {
          "table": "ora_signals",
          "symbol": "SPY",
          "as_of": "2026-10-30",
          "trend": 0.1128,
          "momentum": 0.1658,
          "mostly_up": 0.55,
          "p_up_week": 0.598
        }
      }
    }
  ]
}

macd macdsignal offset sma sum

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Quant developer · example 8

Stochastic %K and %D states for AMD as model inputs

Feed oscillator states into a classifier without writing an oscillator library.

ora-fin-0.1.1AMD5 questions

StateAMD
Questions5 asked
Answersreal, captured
Actionsnone

State

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

Questions

`k_over_d`	rule	`stochk(14) > stochd(14)` stochastic %K above %D
`k_low`	rule	`stochk(14) < 20` Stochastic %K under 20
`willr`	rule	`willr(14) > -20` Williams %R above −20
`cci`	rule	`cci(20) > 100` CCI above 100
`up_tomorrow`	forecast	Will the stock be up tomorrow? Up tomorrow

Answers

Question	Answer	Reading
`k_over_d` stochastic %K above %D	0.3746	false
`k_low` Stochastic %K under 20	0.1754	false
`willr` Williams %R above −20	0.0666	false
`cci` CCI above 100	0.0724	false
`up_tomorrow` Up tomorrow	0.528	53% · close to even

What it means

For AMD on the session of 2026-10-30: Not true: stochastic %K above %D; Stochastic %K under 20; Williams %R above −20; CCI above 100. Forecasts: Up tomorrow 53%.

Oscillator states come back as exact rules; the next-day forecast is the label the classifier is compared with.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "AMD"
  },
  "questions": {
    "k_over_d": {
      "type": "yes_no",
      "ask": "stochk(14) > stochd(14)"
    },
    "k_low": {
      "type": "yes_no",
      "ask": "stochk(14) < 20"
    },
    "willr": {
      "type": "yes_no",
      "ask": "willr(14) > -20"
    },
    "cci": {
      "type": "yes_no",
      "ask": "cci(20) > 100"
    },
    "up_tomorrow": {
      "type": "yes_no",
      "instructions": "Will the stock be up tomorrow?"
    }
  }
}

Response JSON (AMD, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "k_over_d": {
      "yes_no": 0.3746,
      "rule": "stochk(14) > stochd(14)",
      "basis": "rule"
    },
    "k_low": {
      "yes_no": 0.1754,
      "rule": "stochk(14) < 20",
      "basis": "rule"
    },
    "willr": {
      "yes_no": 0.0666,
      "rule": "willr(14) > -20",
      "basis": "rule"
    },
    "cci": {
      "yes_no": 0.0724,
      "rule": "cci(20) > 100",
      "basis": "rule"
    },
    "up_tomorrow": {
      "yes_no": 0.528
    }
  },
  "context": {
    "symbol": "AMD",
    "as_of": "2026-10-30"
  }
}

cci stochd stochk willr

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Quant developer · example 9

Earnings blackout flag for NVDA in a trading pipeline

Block new entries when a report is close, using the earnings calendar facts instead of a separate data feed.

ora-fin-0.1.1NVDA4 questions2 actions

StateNVDA
Questions4 asked
Answersreal, captured
Actions2 rules · dry run

State

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

Questions

`earn_soon`	rule	`days_to_earnings <= 7` earnings within 7 days
`just_reported`	rule	`days_since_earnings <= 5` Reported in the last 5 days
`amc`	English → rule	Does the stock report after the close next time?
`big_mover`	rule	`abs(last_earnings_move) > 5` Last earnings move bigger than 5%

Answers

Question	Answer	Reading
`earn_soon` earnings within 7 days	0.0537	false
`just_reported` Reported in the last 5 days	0.0537	false
`amc` Does the stock report after the close next time compiled to `next_report_amc > 0.5`	0.8591	true
`big_mover` Last earnings move bigger than 5%	0.9463	true

Actions

Rules on the answers decide what to send.

earn_soon >= 0.6→Webhookrisk-gate

else→Webhookrisk-gate

What each action would send 1 of 2 fired · dry run, nothing sent

blackoutrule false: skipped

clearwould send (dry run)Webhook → risk-gate

{
  "symbol": "NVDA",
  "gate": "clear",
  "as_of": "2026-10-30"
}

What it means

For NVDA on the session of 2026-10-30: True now: Does the stock report after the close next time; Last earnings move bigger than 5%. Not true: earnings within 7 days; Reported in the last 5 days. Fired: clear (Webhook → risk-gate).

The gate service gets a blackout or a clear for each symbol; strategies read the gate rather than the calendar.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "NVDA"
  },
  "questions": {
    "earn_soon": {
      "type": "yes_no",
      "ask": "days_to_earnings <= 7"
    },
    "just_reported": {
      "type": "yes_no",
      "ask": "days_since_earnings <= 5"
    },
    "amc": {
      "type": "yes_no",
      "instructions": "Does the stock report after the close next time?"
    },
    "big_mover": {
      "type": "yes_no",
      "ask": "abs(last_earnings_move) > 5"
    }
  },
  "actions": [
    {
      "id": "blackout",
      "when": "earn_soon >= 0.6",
      "do": "webhook",
      "connection": "risk-gate",
      "body": {
        "symbol": "{{symbol}}",
        "gate": "earnings_blackout",
        "p_earn_soon": "{{earn_soon}}",
        "p_big_mover": "{{big_mover}}",
        "as_of": "{{as_of}}"
      }
    },
    {
      "id": "clear",
      "when": "else",
      "do": "webhook",
      "connection": "risk-gate",
      "body": {
        "symbol": "{{symbol}}",
        "gate": "clear",
        "as_of": "{{as_of}}"
      }
    }
  ],
  "dry_run": true
}

Response JSON (NVDA, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "earn_soon": {
      "yes_no": 0.0537,
      "rule": "days_to_earnings <= 7",
      "basis": "rule"
    },
    "just_reported": {
      "yes_no": 0.0537,
      "rule": "days_since_earnings <= 5",
      "basis": "rule"
    },
    "amc": {
      "yes_no": 0.8591,
      "rule": "next_report_amc > 0.5",
      "basis": "pattern"
    },
    "big_mover": {
      "yes_no": 0.9463,
      "rule": "abs(last_earnings_move) > 5",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "NVDA",
    "as_of": "2026-10-30"
  },
  "actions": [
    {
      "id": "blackout",
      "fired": false,
      "status": "skipped"
    },
    {
      "id": "clear",
      "fired": true,
      "status": "dry_run",
      "do": "webhook",
      "connection": "risk-gate",
      "preview": {
        "body": {
          "symbol": "NVDA",
          "gate": "clear",
          "as_of": "2026-10-30"
        }
      }
    }
  ]
}

abs days_since_earnings days_to_earnings last_earnings_move next_report_amc

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Quant developer · example 10

How long since GE last broke out? Timing features with barssince

Measure time-since-event features (breakout, 52-week high, volume spike) for a holding-period model.

ora-fin-0.1.1GE4 questions

StateGE
Questions4 asked
Answersreal, captured
Actionsnone

State

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

Questions

`quiet`	rule	`barssince(C > highest(C, 20)) > 30` No 20-day breakout for 30 sessions
`recent_high`	rule	`highestbars(H, 50) <= 5` the 50-day high was set in the last 5 sessions
`old_low`	rule	`lowestbars(L, 50) >= 30` the 50-day low is at least 30 sessions old
`since_spike`	rule	`barssince(V > 2 * avg(V, 20)) < 10` a volume spike within the last 10 sessions

Answers

Question	Answer	Reading
`quiet` No 20-day breakout for 30 sessions	0.9237	true
`recent_high` the 50-day high was set in the last 5 sessions	0.0563	false
`old_low` the 50-day low is at least 30 sessions old	0.1019	false
`since_spike` a volume spike within the last 10 sessions	0.534	on the line (true)

What it means

For GE on the session of 2026-10-30: True now: No 20-day breakout for 30 sessions. Not true: the 50-day high was set in the last 5 sessions; the 50-day low is at least 30 sessions old. On the line: a volume spike within the last 10 sessions.

Bar-count rules turn event timing into features; a recent high with an old low describes a stock trending away from its base.

Request JSON

{
  "model": "ora-fin-0.1.1",
  "state": {
    "symbol": "GE"
  },
  "questions": {
    "quiet": {
      "type": "yes_no",
      "ask": "barssince(C > highest(C, 20)) > 30"
    },
    "recent_high": {
      "type": "yes_no",
      "ask": "highestbars(H, 50) <= 5"
    },
    "old_low": {
      "type": "yes_no",
      "ask": "lowestbars(L, 50) >= 30"
    },
    "since_spike": {
      "type": "yes_no",
      "ask": "barssince(V > 2 * avg(V, 20)) < 10"
    }
  }
}

Response JSON (GE, real)

{
  "model": "ora-fin-0.1.1",
  "answers": {
    "quiet": {
      "yes_no": 0.9237,
      "rule": "barssince(C > highest(C, 20)) > 30",
      "basis": "rule"
    },
    "recent_high": {
      "yes_no": 0.0563,
      "rule": "highestbars(H, 50) <= 5",
      "basis": "rule"
    },
    "old_low": {
      "yes_no": 0.1019,
      "rule": "lowestbars(L, 50) >= 30",
      "basis": "rule"
    },
    "since_spike": {
      "yes_no": 0.534,
      "rule": "barssince(V > 2 * avg(V, 20)) < 10",
      "basis": "rule"
    }
  },
  "context": {
    "symbol": "GE",
    "as_of": "2026-10-30"
  }
}

avg barssince highest highestbars lowestbars

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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: Fintech app builder, Robo-advisor, Broker app, Algo / bot developer, Data vendor / platform, Trading educator / newsletter.

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

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What it means

Send a QQQ breakout signal to the order service as typed JSON

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Build a regime feature vector for AAPL from exact rules

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Scan a five-name tech universe for oversold-in-uptrend setups

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Regression-test candlestick detectors on TSLA

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What it means

Compare a custom recursive smoother with built-in averages on MSFT

State

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What it means

Nightly signal snapshot for the index ETFs, posted to a data lake hook

State

Questions

Answers

Actions

What it means

Stochastic %K and %D states for AMD as model inputs

State

Questions

Answers

What it means

Earnings blackout flag for NVDA in a trading pipeline

State

Questions

Answers

Actions

What it means

How long since GE last broke out? Timing features with barssince

State

Questions

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What it means