Research

Phase 1: Research

Produce a confirmed, statistically grounded signal spec — indicator params, entry/exit logic, regime conditions — backed by visible data. No controller code until this passes.

Step 1 — Gather market data

run_code, over client.market_data.* — the rows come back as numbers you can put straight into a DataFrame (see the market_data_with_code skill):

df = await client.market_data.get_candles_last_days(connector, pair, days=60, interval="1h")

• connector (e.g. binance_perpetual)
• pair (e.g. BTC-USDT)
• interval — 1h swing, 15m intraday, 1d position
• days — ≥ 60 swing, ≥ 14 intraday, ≥ 180 position

Also fetch in the same snippet, when the pair is a perpetual:

• funding rate (get_funding_info) — reveals directional bias and carry cost
• order book snapshot (get_order_book) — reveals liquidity depth and skew

Step 2 — Compute exploratory indicators

Apply a broad set, then narrow:

import pandas_ta as ta

# Trend
df.ta.ema(length=20, append=True)
df.ta.ema(length=50, append=True)
df.ta.ema(length=200, append=True)
df.ta.adx(length=14, append=True)
df.ta.supertrend(length=10, multiplier=3.0, append=True)

# Momentum
df.ta.rsi(length=14, append=True)
df.ta.macd(fast=12, slow=26, signal=9, append=True)

# Volatility
df.ta.bbands(length=20, std=2.0, append=True)
df.ta.atr(length=14, append=True)

# Custom
df["zscore"] = (df["close"] - df["close"].rolling(20).mean()) / df["close"].rolling(20).std()
df["ret"]    = df["close"].pct_change()
df["rvol"]   = df["ret"].rolling(20).std() * (252 ** 0.5)

Step 3 — Classify the regime

Confirming statistics over ~90d of candles when the table is ambiguous:

• Lag-1 return autocorrelation — positive → trend, negative → mean reversion
• % of bars with ADX > 25
• Hurst exponent (rolling 150 bars) — H > 0.55 trending, H < 0.45 mean-reverting
• Realized volatility — ATR as % of price, 14-period rolling

Verdict: one line — TRENDING, MEAN_REVERTING or AMBIGUOUS, with the supporting values.

Go/no-go: on AMBIGUOUS, either pick a shorter timeframe that resolves it or ask the user whether to force a strategy type. Never build a trend controller on a mean-reverting pair.

Step 4 — Explore indicator combinations

For TRENDING pairs:

• EMA crossover (10/50, 20/100, 50/200)
• MACD (12/26/9) + ADX filter (> 20)
• Supertrend (multiplier 2–4, period 10–14)

For MEAN_REVERTING pairs:

• RSI extremes (period 14, levels 30/70 or 20/80)
• Bollinger %B (band touch/cross, 20/2.0)
• Rolling Z-score (period 30–60, threshold ±1.5–2.5)

For each combination compute:

• Signal frequency — signals per week
• Average bars-in-trade — consecutive bars holding the same signal
• Directional accuracy — % of signals followed by a 1R move the right way

Go/no-go: need ≥ 2 signals/week on the chosen timeframe and ≥ 55% raw directional accuracy before continuing.

Step 5 — Write the signal spec

signal_spec:
  type: TREND | MEAN_REVERSION
  indicator_1: name, params
  indicator_2: name, params        # optional filter
  long_condition:  <pandas boolean expression>
  short_condition: <pandas boolean expression>
  exit_condition:  <pandas boolean expression or None (use TP/SL)>
  timeframe: 1h | 4h | 1d
  lookback_bars: 300–500

The user must confirm this spec before Phase 2.

Step 6 — Data quality check

Before handing off:

• No NaN bleed at the start of the window — drop the first max(lookback) bars
• No look-ahead bias — every computation that decides an entry bar uses .shift(1) where needed
• Signal frequency over the last 30 days matches the in-sample estimate (±50%)

Step 7 — Research routine (optional)

If the user wants ongoing monitoring, write it yourself — read the routine_cookbook playbook first, then create and test:

manage_skill(action="read", name="routine_cookbook")
manage_routines(action="create_routine", name="{pair}_research", code="<python>")
manage_routines(action="run", name="{pair}_research")

It should fetch the candles, compute the indicators, mark the signal column, and report the last ~20 bars plus a regime summary. Fix it until the run is clean before showing the user.

Go/No-Go → Phase 2

✅ GO — regime identified with supporting data; a signal hypothesis with > 30 historical occurrences in the lookback; signal duration matches the intended holding period; no liquidity or funding red flags.

❌ NO-GO — ADX < 15 chop with no mean-reversion setup; fewer than 20 historical signal occurrences; pair under $1M daily volume; funding > 0.1%/8h against the dominant signal direction.

Artifacts

1. Market data summary (price range, volume, volatility stats)
2. Indicator dashboard (last 10–20 bars, all values)
3. Regime verdict + evidence
4. Signal spec (confirmed by the user)
5. Data quality check results
6. Research routine (optional)