feat(phase-3): paper-trading runner BTC+ETH

Modulo paper_trading per forward-test virtuale Phase 3:
- Portfolio multi-asset equal-weight sleeve, fees bp su round-trip
- PaperExecutor compila strategia JSON e applica segnale a bar close
- PaperRepository persiste runs/ticks/trades/equity in runs.db
- CLI scripts/run_paper_trading.py: loop polling Cerbero, exec su nuovo bar

Strategie deployate:
- BTC fb63e851 (Sharpe OOS +0.50, mean rev RSI+ATR+hour gate)
- ETH facd6af85d5d (Sharpe OOS +0.19, trend vol regime + SMA50/200)

Capitale virtuale $1000 (sleeve $500 ciascuno), 2 settimane smoke.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-05-13 23:34:15 +02:00
parent 9d1ef8adcf
commit 45f273f591
9 changed files with 881 additions and 0 deletions
+202
View File
@@ -0,0 +1,202 @@
"""Paper-trading runner Phase 3 — forward-test virtuale BTC + ETH.
Loop infinito (o limitato via --max-ticks) che ogni ``--poll-seconds``:
1. fetch OHLCV 1h ultime ~500 barre via Cerbero
2. per ogni strategia: compile + esegui ultimo bar
3. apply segnale al portfolio multi-asset
4. snapshot equity in DB
I bar 1h chiudono al minuto :00. Il loop riconosce un "nuovo bar chiuso"
confrontando l'ultimo timestamp del DataFrame con quello dell'iterazione
precedente. Tick consecutivi su stesso bar = hold (no double-trade).
Esempio:
uv run python scripts/run_paper_trading.py \
--name phase3-papertrade-001 \
--initial-capital 1000 \
--max-ticks 336 # 2 settimane * 24 ore
"""
from __future__ import annotations
import argparse
import time
from dataclasses import dataclass
from datetime import UTC, datetime, timedelta
from pathlib import Path
from multi_swarm.cerbero.client import CerberoClient
from multi_swarm.config import load_settings
from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
from multi_swarm.paper_trading.executor import PaperExecutor
from multi_swarm.paper_trading.persistence import PaperRepository
from multi_swarm.paper_trading.portfolio import Portfolio
from multi_swarm.persistence.repository import Repository
PROJECT_ROOT = Path(__file__).resolve().parent.parent
@dataclass(frozen=True)
class AssetConfig:
symbol: str # es. "BTC-PERPETUAL"
strategy_file: Path
exchange: str = "deribit"
timeframe: str = "1h"
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Paper-trading runner Phase 3")
p.add_argument("--name", default="phase3-papertrade-001")
p.add_argument("--initial-capital", type=float, default=1000.0)
p.add_argument("--fees-bp", type=float, default=5.0)
p.add_argument("--poll-seconds", type=int, default=300, help="Polling interval (5min default)")
p.add_argument("--max-ticks", type=int, default=0, help="0 = infinito; per smoke test usa 1")
p.add_argument("--lookback-bars", type=int, default=500, help="Quante bar fetchare per indicatori")
p.add_argument(
"--strategies-dir",
default=str(PROJECT_ROOT / "strategies"),
help="Cartella contenente btc_*.json e eth_*.json",
)
return p.parse_args()
def load_assets(strategies_dir: Path) -> list[AssetConfig]:
btc_files = sorted(strategies_dir.glob("btc_*.json"))
eth_files = sorted(strategies_dir.glob("eth_*.json"))
if not btc_files or not eth_files:
raise FileNotFoundError(
f"Expected btc_*.json and eth_*.json in {strategies_dir}"
)
return [
AssetConfig(symbol="BTC-PERPETUAL", strategy_file=btc_files[0]),
AssetConfig(symbol="ETH-PERPETUAL", strategy_file=eth_files[0]),
]
def main() -> None:
args = parse_args()
settings = load_settings()
# Inizializza schema (idempotente).
Repository(settings.db_path).init_schema()
token = (
settings.cerbero_mainnet_token.get_secret_value()
if settings.cerbero_mainnet_token
else settings.cerbero_testnet_token.get_secret_value()
)
cerbero = CerberoClient(
base_url=settings.cerbero_base_url,
token=token,
bot_tag=settings.cerbero_bot_tag,
)
loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
assets = load_assets(Path(args.strategies_dir))
executors: list[PaperExecutor] = [
PaperExecutor(strategy_json_path=a.strategy_file, symbol=a.symbol) for a in assets
]
print(f"Loaded {len(assets)} strategies:")
for a, ex in zip(assets, executors, strict=True):
print(f" {a.symbol}: {a.strategy_file.name} -> {len(ex._strategy.rules)} rules")
portfolio = Portfolio(
initial_capital=args.initial_capital,
fees_bp=args.fees_bp,
n_sleeves=len(assets),
)
repo = PaperRepository(settings.db_path)
config = {
"assets": [
{"symbol": a.symbol, "strategy": a.strategy_file.name, "exchange": a.exchange}
for a in assets
],
"fees_bp": args.fees_bp,
"poll_seconds": args.poll_seconds,
"lookback_bars": args.lookback_bars,
}
run_id = repo.create_run(
name=args.name, initial_capital=args.initial_capital, config=config
)
print(f"Paper run started: {run_id} ({args.name})")
print(f" initial_capital=${args.initial_capital:.2f}, sleeve=${portfolio.sleeve_capital:.2f}")
tick_count = 0
last_bars_seen: dict[str, datetime] = {}
try:
while args.max_ticks == 0 or tick_count < args.max_ticks:
now = datetime.now(UTC)
last_prices: dict[str, float] = {}
for asset, executor in zip(assets, executors, strict=True):
# fetch OHLCV most recent lookback bars
end = now.replace(minute=0, second=0, microsecond=0)
start = end - timedelta(hours=args.lookback_bars + 1)
req = OHLCVRequest(
symbol=asset.symbol,
timeframe=asset.timeframe,
start=start,
end=end,
exchange=asset.exchange,
)
# bypass cache for live data
try:
ohlcv = loader._fetch(req) # noqa: SLF001
except Exception as e: # noqa: BLE001
print(f"[{now.isoformat()}] {asset.symbol} fetch FAIL: {e}")
continue
if len(ohlcv) < 10:
print(f"[{now.isoformat()}] {asset.symbol} too few bars ({len(ohlcv)})")
continue
bar_ts = ohlcv.index[-1]
last_bar_dt = bar_ts.to_pydatetime() if hasattr(bar_ts, "to_pydatetime") else bar_ts
# skip se barra gia' processata in questo tick
if last_bars_seen.get(asset.symbol) == last_bar_dt:
last_prices[asset.symbol] = float(ohlcv["close"].iloc[-1])
continue
last_bars_seen[asset.symbol] = last_bar_dt
result = executor.execute_tick(portfolio, ohlcv, now)
repo.save_tick(run_id, result)
last_prices[asset.symbol] = result.close_price
if result.action_taken != "hold":
pnl_str = (
f"pnl=${result.trade.net_pnl:+.2f}" if result.trade else ""
)
print(
f"[{now.isoformat()}] {asset.symbol} bar={last_bar_dt} "
f"close={result.close_price:.2f} signal={result.signal.value} "
f"action={result.action_taken} {pnl_str}"
)
repo.sync_open_positions(run_id, portfolio)
eq, pos_val = portfolio.equity(last_prices)
repo.save_equity_snapshot(run_id, now, eq, portfolio.cash, pos_val)
tick_count += 1
print(
f"[{now.isoformat()}] tick={tick_count} "
f"equity=${eq:.2f} cash=${portfolio.cash:.2f} pos_val=${pos_val:.2f} "
f"open={list(portfolio.positions.keys())}"
)
if args.max_ticks > 0 and tick_count >= args.max_ticks:
break
time.sleep(args.poll_seconds)
repo.stop_run(run_id, status="completed")
except KeyboardInterrupt:
print("\nInterrupted by user")
repo.stop_run(run_id, status="interrupted")
except Exception as e: # noqa: BLE001
print(f"Run failed: {e}")
repo.stop_run(run_id, status="failed")
raise
print(f"Paper run {run_id} stopped after {tick_count} ticks")
print(f"Final equity: ${portfolio.equity({})[0]:.2f}")
print(f"Trades closed: {len(portfolio.closed_trades)}")
if __name__ == "__main__":
main()
+96
View File
@@ -0,0 +1,96 @@
"""PaperExecutor: applica un segnale di strategia a un Portfolio.
Il flusso per ogni tick:
bar OHLCV chiuso -> compile_strategy(strategy) -> Series[Side]
-> last_signal = series.iloc[-1]
-> match con posizione attuale -> open / close / hold
Niente delay 1-bar: in paper-trading il segnale viene calcolato sulla
barra appena chiusa e applicato al prezzo close della stessa. La latenza
reale tra tick e ordine va misurata separatamente (Phase 3 spec).
"""
from __future__ import annotations
import json
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
import pandas as pd # type: ignore[import-untyped]
from ..backtest.orders import Side, Trade
from ..protocol.compiler import compile_strategy
from ..protocol.parser import parse_strategy
from .portfolio import OpenPosition, Portfolio
@dataclass
class TickResult:
ts: datetime
symbol: str
bar_ts: datetime
close_price: float
signal: Side
action_taken: str # "open_long" | "open_short" | "close" | "reverse" | "hold"
trade: Trade | None = None
new_position: OpenPosition | None = None
class PaperExecutor:
def __init__(self, strategy_json_path: Path, symbol: str) -> None:
text = strategy_json_path.read_text()
# parse_strategy si aspetta JSON pulito, non fence; il file e' gia' JSON.
self._strategy = parse_strategy(text)
self._compiled = compile_strategy(self._strategy)
self.symbol = symbol
self.strategy_path = strategy_json_path
def execute_tick(
self,
portfolio: Portfolio,
ohlcv: pd.DataFrame,
now: datetime,
) -> TickResult:
"""Esegui un tick: calcola segnale su tutto ``ohlcv`` (per indicatori
con lookback), prendi l'ultimo, e applica al portfolio."""
if len(ohlcv) == 0:
raise ValueError("Empty OHLCV passed to execute_tick")
signals = self._compiled(ohlcv)
# ultimo bar chiuso
bar_ts = ohlcv.index[-1]
close_price = float(ohlcv["close"].iloc[-1])
signal = Side(signals.iloc[-1]) if signals.iloc[-1] is not None else Side.FLAT
current = portfolio.positions.get(self.symbol)
action = "hold"
trade: Trade | None = None
new_position: OpenPosition | None = None
if current is None and signal != Side.FLAT:
new_position = portfolio.open(self.symbol, signal, close_price, now)
action = f"open_{signal.value}"
elif current is not None and signal == Side.FLAT:
trade = portfolio.close(self.symbol, close_price, now)
action = "close"
elif current is not None and signal != current.side:
# reverse: chiudi e riapri opposto
trade = portfolio.close(self.symbol, close_price, now)
new_position = portfolio.open(self.symbol, signal, close_price, now)
action = "reverse"
return TickResult(
ts=now,
symbol=self.symbol,
bar_ts=bar_ts.to_pydatetime() if hasattr(bar_ts, "to_pydatetime") else bar_ts,
close_price=close_price,
signal=signal,
action_taken=action,
trade=trade,
new_position=new_position,
)
@property
def strategy_dict(self) -> dict:
return json.loads(self.strategy_path.read_text())
@@ -0,0 +1,114 @@
"""Persistenza paper-trading: usa lo stesso ``runs.db`` con tabelle dedicate
``paper_trading_*`` (vedi :mod:`multi_swarm.persistence.schema`).
"""
from __future__ import annotations
import json
import sqlite3
import uuid
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from .executor import TickResult
from .portfolio import Portfolio
class PaperRepository:
def __init__(self, db_path: Path | str):
self.db_path = Path(db_path)
def _conn(self) -> sqlite3.Connection:
conn = sqlite3.connect(self.db_path, isolation_level=None)
conn.row_factory = sqlite3.Row
conn.execute("PRAGMA foreign_keys = ON")
conn.execute("PRAGMA journal_mode = WAL")
return conn
@staticmethod
def _now() -> str:
return datetime.now(UTC).isoformat()
def create_run(self, name: str, initial_capital: float, config: dict[str, Any]) -> str:
rid = uuid.uuid4().hex
with self._conn() as conn:
conn.execute(
"INSERT INTO paper_trading_runs "
"(id, name, started_at, status, initial_capital, config_json) "
"VALUES (?,?,?,?,?,?)",
(rid, name, self._now(), "running", initial_capital, json.dumps(config)),
)
return rid
def stop_run(self, run_id: str, status: str = "stopped") -> None:
with self._conn() as conn:
conn.execute(
"UPDATE paper_trading_runs SET stopped_at=?, status=? WHERE id=?",
(self._now(), status, run_id),
)
def save_tick(self, run_id: str, tick: TickResult) -> None:
with self._conn() as conn:
conn.execute(
"INSERT INTO paper_trading_ticks "
"(paper_run_id, symbol, ts, bar_ts, close_price, signal, action_taken) "
"VALUES (?,?,?,?,?,?,?)",
(
run_id,
tick.symbol,
tick.ts.isoformat(),
tick.bar_ts.isoformat() if hasattr(tick.bar_ts, "isoformat") else str(tick.bar_ts),
tick.close_price,
tick.signal.value,
tick.action_taken,
),
)
if tick.trade is not None:
t = tick.trade
conn.execute(
"INSERT INTO paper_trading_trades "
"(paper_run_id, symbol, side, qty, entry_price, exit_price, "
"entry_ts, exit_ts, pnl, fees) VALUES (?,?,?,?,?,?,?,?,?,?)",
(
run_id,
tick.symbol,
t.side.value,
t.size,
t.entry_price,
t.exit_price,
t.entry_ts.isoformat(),
t.exit_ts.isoformat(),
t.net_pnl,
t.fees,
),
)
def save_equity_snapshot(
self,
run_id: str,
ts: datetime,
equity: float,
cash: float,
positions_value: float,
) -> None:
with self._conn() as conn:
conn.execute(
"INSERT INTO paper_trading_equity "
"(paper_run_id, ts, equity, cash, positions_value) VALUES (?,?,?,?,?)",
(run_id, ts.isoformat(), equity, cash, positions_value),
)
def sync_open_positions(self, run_id: str, portfolio: Portfolio) -> None:
"""Sostituisce snapshot posizioni aperte. Idempotente: cancella e reinserisce."""
with self._conn() as conn:
conn.execute(
"DELETE FROM paper_trading_positions WHERE paper_run_id=?", (run_id,)
)
for sym, pos in portfolio.positions.items():
conn.execute(
"INSERT INTO paper_trading_positions "
"(paper_run_id, symbol, side, qty, entry_price, entry_ts) "
"VALUES (?,?,?,?,?,?)",
(run_id, sym, pos.side.value, pos.qty, pos.entry_price, pos.entry_ts.isoformat()),
)
+104
View File
@@ -0,0 +1,104 @@
"""Portfolio multi-asset per paper-trading.
Modello semplificato: capitale unico ``cash``, allocazione equal-weight
fra N posizioni (sleeve = 1/N del capitale iniziale per ogni simbolo).
Niente leva, niente liquidation, fees su entry+exit (bp del notional).
Una :class:`Position` rappresenta una posizione aperta su un singolo
simbolo (long/short, qty in unita' dell'asset, prezzo di entry). La
posizione viene chiusa con :meth:`Portfolio.close` che produce un
:class:`Trade` realized e accredita ``cash``.
Mark-to-market via :meth:`Portfolio.equity`.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from datetime import datetime
from ..backtest.orders import Side, Trade
@dataclass(frozen=True)
class OpenPosition:
symbol: str
side: Side
qty: float
entry_price: float
entry_ts: datetime
@dataclass
class Portfolio:
initial_capital: float
fees_bp: float = 5.0
n_sleeves: int = 2 # numero strategie / asset previsti
cash: float = field(init=False)
positions: dict[str, OpenPosition] = field(default_factory=dict)
closed_trades: list[Trade] = field(default_factory=list)
def __post_init__(self) -> None:
self.cash = self.initial_capital
@property
def sleeve_capital(self) -> float:
return self.initial_capital / self.n_sleeves
def open(
self,
symbol: str,
side: Side,
price: float,
ts: datetime,
) -> OpenPosition:
if symbol in self.positions:
raise ValueError(f"Position already open on {symbol}")
if side == Side.FLAT:
raise ValueError("Cannot open a FLAT position")
# sleeve fisso: alloca 1/n_sleeves del capitale iniziale, qty = notional/price.
notional = self.sleeve_capital
qty = notional / price
fees = notional * (self.fees_bp / 10000.0)
self.cash -= fees
pos = OpenPosition(symbol=symbol, side=side, qty=qty, entry_price=price, entry_ts=ts)
self.positions[symbol] = pos
return pos
def close(
self,
symbol: str,
price: float,
ts: datetime,
) -> Trade:
if symbol not in self.positions:
raise ValueError(f"No open position on {symbol}")
pos = self.positions.pop(symbol)
trade = Trade(
entry_ts=pos.entry_ts,
exit_ts=ts,
side=pos.side,
size=pos.qty,
entry_price=pos.entry_price,
exit_price=price,
fees_bp=self.fees_bp,
)
# net_pnl include gia' i fees sull'intero round-trip; abbiamo gia'
# addebitato meta' fees all'open, ora addebitiamo il resto.
self.cash += trade.gross_pnl - (trade.fees / 2.0)
self.closed_trades.append(trade)
return trade
def equity(self, last_prices: dict[str, float]) -> tuple[float, float]:
"""Ritorna (equity_totale, positions_value) marcando posizioni aperte
al ``last_prices[symbol]``. Posizioni senza prezzo disponibile valgono
notional di entry (fallback conservativo)."""
positions_value = 0.0
for sym, pos in self.positions.items():
price = last_prices.get(sym, pos.entry_price)
unreal = pos.qty * (
price - pos.entry_price if pos.side == Side.LONG
else pos.entry_price - price
)
positions_value += pos.qty * pos.entry_price + unreal
return self.cash + positions_value, positions_value
+61
View File
@@ -77,7 +77,68 @@ CREATE TABLE IF NOT EXISTS adversarial_findings (
FOREIGN KEY (run_id) REFERENCES runs(id)
);
CREATE TABLE IF NOT EXISTS paper_trading_runs (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
started_at TEXT NOT NULL,
stopped_at TEXT,
status TEXT NOT NULL DEFAULT 'running',
initial_capital REAL NOT NULL,
config_json TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS paper_trading_positions (
paper_run_id TEXT NOT NULL,
symbol TEXT NOT NULL,
side TEXT NOT NULL,
qty REAL NOT NULL,
entry_price REAL NOT NULL,
entry_ts TEXT NOT NULL,
PRIMARY KEY (paper_run_id, symbol),
FOREIGN KEY (paper_run_id) REFERENCES paper_trading_runs(id)
);
CREATE TABLE IF NOT EXISTS paper_trading_trades (
id INTEGER PRIMARY KEY AUTOINCREMENT,
paper_run_id TEXT NOT NULL,
symbol TEXT NOT NULL,
side TEXT NOT NULL,
qty REAL NOT NULL,
entry_price REAL NOT NULL,
exit_price REAL NOT NULL,
entry_ts TEXT NOT NULL,
exit_ts TEXT NOT NULL,
pnl REAL NOT NULL,
fees REAL NOT NULL,
FOREIGN KEY (paper_run_id) REFERENCES paper_trading_runs(id)
);
CREATE TABLE IF NOT EXISTS paper_trading_equity (
id INTEGER PRIMARY KEY AUTOINCREMENT,
paper_run_id TEXT NOT NULL,
ts TEXT NOT NULL,
equity REAL NOT NULL,
cash REAL NOT NULL,
positions_value REAL NOT NULL,
FOREIGN KEY (paper_run_id) REFERENCES paper_trading_runs(id)
);
CREATE TABLE IF NOT EXISTS paper_trading_ticks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
paper_run_id TEXT NOT NULL,
symbol TEXT NOT NULL,
ts TEXT NOT NULL,
bar_ts TEXT NOT NULL,
close_price REAL NOT NULL,
signal TEXT NOT NULL,
action_taken TEXT NOT NULL,
FOREIGN KEY (paper_run_id) REFERENCES paper_trading_runs(id)
);
CREATE INDEX IF NOT EXISTS idx_evaluations_fitness ON evaluations(run_id, fitness DESC);
CREATE INDEX IF NOT EXISTS idx_genomes_generation ON genomes(run_id, generation_idx);
CREATE INDEX IF NOT EXISTS idx_cost_run ON cost_records(run_id);
CREATE INDEX IF NOT EXISTS idx_paper_trades_run ON paper_trading_trades(paper_run_id, exit_ts);
CREATE INDEX IF NOT EXISTS idx_paper_equity_run ON paper_trading_equity(paper_run_id, ts);
CREATE INDEX IF NOT EXISTS idx_paper_ticks_run ON paper_trading_ticks(paper_run_id, ts);
"""
+142
View File
@@ -0,0 +1,142 @@
{
"rules": [
{
"condition": {
"op": "and",
"args": [
{
"op": "gt",
"args": [
{
"kind": "indicator",
"name": "rsi",
"params": [
14
]
},
{
"kind": "literal",
"value": 70.0
}
]
},
{
"op": "gt",
"args": [
{
"kind": "indicator",
"name": "atr",
"params": [
14
]
},
{
"kind": "indicator",
"name": "sma",
"params": [
14
]
}
]
},
{
"op": "gt",
"args": [
{
"kind": "feature",
"name": "hour"
},
{
"kind": "literal",
"value": 9
}
]
},
{
"op": "lt",
"args": [
{
"kind": "feature",
"name": "hour"
},
{
"kind": "literal",
"value": 17
}
]
}
]
},
"action": "entry-short"
},
{
"condition": {
"op": "and",
"args": [
{
"op": "lt",
"args": [
{
"kind": "indicator",
"name": "rsi",
"params": [
14
]
},
{
"kind": "literal",
"value": 30.0
}
]
},
{
"op": "lt",
"args": [
{
"kind": "indicator",
"name": "atr",
"params": [
14
]
},
{
"kind": "indicator",
"name": "sma",
"params": [
14
]
}
]
},
{
"op": "gt",
"args": [
{
"kind": "feature",
"name": "hour"
},
{
"kind": "literal",
"value": 9
}
]
},
{
"op": "lt",
"args": [
{
"kind": "feature",
"name": "hour"
},
{
"kind": "literal",
"value": 17
}
]
}
]
},
"action": "entry-long"
}
]
}
+162
View File
@@ -0,0 +1,162 @@
{
"rules": [
{
"condition": {
"op": "and",
"args": [
{
"op": "gt",
"args": [
{
"kind": "indicator",
"name": "atr",
"params": [
14
]
},
{
"kind": "literal",
"value": 0.02
}
]
},
{
"op": "gt",
"args": [
{
"kind": "indicator",
"name": "realized_vol",
"params": [
20
]
},
{
"kind": "literal",
"value": 0.03
}
]
},
{
"op": "gt",
"args": [
{
"kind": "indicator",
"name": "sma",
"params": [
50
]
},
{
"kind": "indicator",
"name": "sma",
"params": [
200
]
}
]
}
]
},
"action": "entry-long"
},
{
"condition": {
"op": "and",
"args": [
{
"op": "lt",
"args": [
{
"kind": "indicator",
"name": "atr",
"params": [
14
]
},
{
"kind": "literal",
"value": 0.01
}
]
},
{
"op": "lt",
"args": [
{
"kind": "indicator",
"name": "realized_vol",
"params": [
20
]
},
{
"kind": "literal",
"value": 0.02
}
]
},
{
"op": "lt",
"args": [
{
"kind": "indicator",
"name": "sma",
"params": [
50
]
},
{
"kind": "indicator",
"name": "sma",
"params": [
200
]
}
]
}
]
},
"action": "entry-short"
},
{
"condition": {
"op": "or",
"args": [
{
"op": "crossover",
"args": [
{
"kind": "indicator",
"name": "rsi",
"params": [
14
]
},
{
"kind": "literal",
"value": 70.0
}
]
},
{
"op": "crossunder",
"args": [
{
"kind": "indicator",
"name": "rsi",
"params": [
14
]
},
{
"kind": "literal",
"value": 30.0
}
]
}
]
},
"action": "exit"
}
]
}