tradepose-models 1.1.0

Shared Pydantic models for TradePose platform

TradePose Models

Shared Pydantic models, enums, and schemas for the TradePose quantitative trading platform.

What is this?

Core data models shared across all TradePose packages (gateway, client, workers). Provides:

• Strategy Configuration - Complete strategy definition with blueprints and triggers
• Registry & Portfolio - Multi-strategy management with unique key indexing
• Enumerations - Type-safe enums for frequencies, directions, platforms
• Polars Schemas - DataFrame schemas for trades, performance, OHLCV
• Type Safety - Full Pydantic validation with IDE autocomplete

Installation

pip install tradepose-models

Requirements:

• Python 3.13+
• Dependencies: pydantic, polars

---

Quick Start

Strategy Configuration

from tradepose_models.strategy import (
    StrategyConfig,
    Blueprint,
    Trigger,
    IndicatorSpec,
)
from tradepose_models.enums import (
    Freq,
    TradeDirection,
    TrendType,
    OrderStrategy,
)
from tradepose_models.indicators import Indicator
import polars as pl

# Create indicator
atr_spec = IndicatorSpec(
    freq=Freq.DAY_1,
    shift=1,
    indicator=Indicator.atr(period=14),
)

# Create trigger
entry_trigger = Trigger(
    name="breakout_entry",
    order_strategy=OrderStrategy.IMMEDIATE_ENTRY,
    priority=1,
    conditions=[pl.col("close") > pl.col("high").shift(1)],
    price_expr=pl.col("close"),
)

# Create blueprint
blueprint = Blueprint(
    name="trend_long",
    direction=TradeDirection.LONG,
    trend_type=TrendType.TREND,
    entry_first=True,
    entry_triggers=[entry_trigger],
    exit_triggers=[...],
)

# Create strategy
strategy = StrategyConfig(
    name="ES_Breakout",
    base_instrument="ES",
    base_freq=Freq.MIN_15,
    note="E-mini S&P breakout strategy",
    indicators=[atr_spec],
    base_blueprint=blueprint,
)

# Save/Load
strategy.save("strategy.json")
loaded = StrategyConfig.load("strategy.json")

Strategy Registry

Manage multiple strategies with (strategy_name, blueprint_name) unique keys:

from tradepose_models.strategy import StrategyRegistry, StrategyConfig

# Create registry
registry = StrategyRegistry()

# Add strategy (auto-splits blueprints into separate entries)
strategy = StrategyConfig.load("multi_blueprint_strategy.json")
keys = registry.add(strategy)
# keys = [(BlueprintSelection("MyStrategy", "long_bp"), BlueprintSelection("MyStrategy", "short_bp")]

# Access entries
entry = registry.get("MyStrategy", "long_bp")
print(entry.blueprint.direction)  # TradeDirection.LONG

# Get all configs for backtesting
configs = registry.get_configs()  # List[StrategyConfig] with single blueprints

Portfolio Selection

Create portfolios by selecting from registry:

from tradepose_models.strategy import StrategyRegistry

registry = StrategyRegistry()
registry.add(strategy1)  # 2 blueprints
registry.add(strategy2)  # 3 blueprints

# Create portfolio (selection view)
portfolio = registry.select(
    name="Q1_Momentum",
    selections=[
        ("Strategy1", "momentum_long"),
        ("Strategy1", "momentum_short"),
        ("Strategy2", "trend_follow"),
    ],
    capital=100000,
    currency="USD",
    platform="MT5",
)

# Get configs for BatchTester
configs = portfolio.get_configs(registry)

# Use with BatchTester
from tradepose_client import BatchTester
from tradepose_client.batch import Period

tester = BatchTester(api_key="tp_xxx")
batch = tester.submit(strategies=configs, periods=[Period.Q1(2024)])
batch.wait()

---

Core Concepts

Strategy Hierarchy

StrategyConfig
├── name, base_instrument, base_freq
├── indicators: List[IndicatorSpec]
├── base_blueprint: Blueprint
│   ├── name, direction, trend_type
│   ├── entry_triggers: List[Trigger]
│   │   └── conditions: List[pl.Expr], price_expr: pl.Expr
│   └── exit_triggers: List[Trigger]
└── advanced_blueprints: List[Blueprint]

Registry & Portfolio

StrategyRegistry (storage, guarantees uniqueness)
│
│  Key: (strategy_name, blueprint_name)
│
├── add(config) → auto-split blueprints
├── get(name, bp) → RegistryEntry
├── get_configs() → List[StrategyConfig]  ──> for BatchTester
└── select(...) → Portfolio
                     │
                     ▼
              Portfolio (selection view)
              ├── selections: List[(name, bp)]
              ├── capital, currency, platform
              └── get_configs(registry) → configs

---

API Reference

Enumerations

Freq (Time Frequency)

from tradepose_models.enums import Freq

Freq.MIN_1   # "1min"
Freq.MIN_5   # "5min"
Freq.MIN_15  # "15min"
Freq.MIN_30  # "30min"
Freq.HOUR_1  # "1h"
Freq.HOUR_4  # "4h"
Freq.DAY_1   # "1D"
Freq.WEEK_1  # "1W"
Freq.MONTH_1 # "1M"

TradeDirection

from tradepose_models.enums import TradeDirection

TradeDirection.LONG   # Long trades only
TradeDirection.SHORT  # Short trades only
TradeDirection.BOTH   # Both directions

TrendType

from tradepose_models.enums import TrendType

TrendType.TREND     # Trend-following
TrendType.RANGE     # Mean-reversion
TrendType.REVERSAL  # Counter-trend

OrderStrategy

from tradepose_models.enums import OrderStrategy

# Entry
OrderStrategy.IMMEDIATE_ENTRY      # Execute immediately
OrderStrategy.FAVORABLE_DELAY_ENTRY  # Wait for pullback
OrderStrategy.ADVERSE_DELAY_ENTRY   # Wait for breakout

# Exit
OrderStrategy.IMMEDIATE_EXIT  # Exit immediately
OrderStrategy.STOP_LOSS       # Fixed stop loss
OrderStrategy.TAKE_PROFIT     # Fixed profit target
OrderStrategy.TRAILING_STOP   # Dynamic trailing
OrderStrategy.BREAKEVEN       # Move to breakeven
OrderStrategy.TIMEOUT_EXIT    # Time-based exit

Currency

from tradepose_models.enums import Currency

Currency.USD, Currency.USDT, Currency.TWD
Currency.EUR, Currency.JPY
Currency.BTC, Currency.ETH
Currency.XAU, Currency.TAIEX

Platform

from tradepose_models.enums import Platform

Platform.MT5      # MetaTrader 5
Platform.BINANCE  # Binance
Platform.SHIOAJI  # Shioaji (Taiwan)
Platform.CCXT     # CCXT unified

AccountSource

from tradepose_models.enums import AccountSource

AccountSource.FTMO        # tz_offset: 2
AccountSource.IB          # tz_offset: 0
AccountSource.FIVEPERCENT # tz_offset: 2
AccountSource.BINANCE     # tz_offset: 8
AccountSource.SHIOAJI     # tz_offset: 8

# Get timezone offset
offset = AccountSource.FTMO.tz_offset()  # 2

Strategy Models

StrategyConfig

from tradepose_models.strategy import StrategyConfig

# Fields
strategy.name              # str: Strategy name
strategy.base_instrument   # str: Trading instrument
strategy.base_freq         # Freq: Base timeframe
strategy.note              # str: Description
strategy.indicators        # List[IndicatorSpec]
strategy.base_blueprint    # Blueprint: Primary blueprint
strategy.advanced_blueprints  # List[Blueprint]: Additional blueprints

# Methods
StrategyConfig.load(path)     # Load from JSON file
strategy.save(path)           # Save to JSON file
strategy.to_json()            # Serialize to JSON string
strategy.to_dict()            # Convert to dict
StrategyConfig.from_api(data) # Parse from API response

Blueprint

from tradepose_models.strategy import Blueprint

# Fields
blueprint.name           # str: Blueprint name
blueprint.direction      # TradeDirection: LONG/SHORT/BOTH
blueprint.trend_type     # TrendType: TREND/RANGE/REVERSAL
blueprint.entry_first    # bool: Entry signals priority
blueprint.entry_triggers # List[Trigger]
blueprint.exit_triggers  # List[Trigger]

Trigger

from tradepose_models.strategy import Trigger
import polars as pl

trigger = Trigger(
    name="breakout",
    order_strategy=OrderStrategy.IMMEDIATE_ENTRY,
    priority=1,
    conditions=[
        pl.col("close") > pl.col("high").shift(1),
        pl.col("volume") > pl.col("volume").rolling_mean(20),
    ],
    price_expr=pl.col("close"),
)

# Access Polars expressions directly
expr = trigger.conditions[0]  # pl.Expr

IndicatorSpec

from tradepose_models.strategy import IndicatorSpec
from tradepose_models.indicators import Indicator

spec = IndicatorSpec(
    freq=Freq.DAY_1,
    shift=1,
    indicator=Indicator.atr(period=14),
)

# Properties
spec.short_name()    # "ATR|14"
spec.display_name()  # "1D_ATR|14_s1"
spec.col()           # pl.col("1D_ATR|14_s1")

Registry Models

StrategyRegistry

from tradepose_models.strategy import StrategyRegistry

registry = StrategyRegistry()

# Add strategy (auto-splits blueprints)
keys = registry.add(strategy)  # Returns List[BlueprintSelection]

# Add or replace
keys = registry.add_or_replace(strategy)

# Get entry
entry = registry.get("StrategyName", "blueprint_name")

# Get original strategy (with all blueprints)
original = registry.get_strategy("StrategyName")

# Get all configs (single-blueprint each)
configs = registry.get_configs()

# Remove strategy
removed_count = registry.remove("StrategyName")

# Create portfolio
portfolio = registry.select(
    name="MyPortfolio",
    selections=[("Strat1", "bp1"), ("Strat2", "bp2")],
    capital=100000,
)

# Iteration
for entry in registry:
    print(entry.key)

# Membership
if ("StrategyName", "blueprint") in registry:
    ...

# Length
print(f"Entries: {len(registry)}")

Portfolio

from tradepose_models.strategy import Portfolio, BlueprintSelection

# Create directly
portfolio = Portfolio(
    name="Q1_Portfolio",
    selections=[BlueprintSelection(strategy_name="Strat1", blueprint_name="bp1")],
    capital=100000,
    currency="USD",
    account_source="FTMO",
    platform="MT5",
)

# Or via registry.select()
portfolio = registry.select(...)

# Get configs for backtesting
configs = portfolio.get_configs(registry)

# Immutable operations (return new instance)
new_portfolio = portfolio.add_selection("Strat2", "bp2")
new_portfolio = portfolio.remove_selection("Strat1", "bp1")

# Properties
portfolio.strategy_names     # List[str] unique names
portfolio.selection_count    # int

# Serialization
portfolio.save("portfolio.json")
loaded = Portfolio.load("portfolio.json")
portfolio.to_json()
portfolio.to_dict()

Indicators

Indicator Factory

from tradepose_models.indicators import Indicator

# Moving Averages
Indicator.sma(period=20, column="close")
Indicator.ema(period=20, column="close")
Indicator.smma(period=20, column="close")
Indicator.wma(period=20, column="close")

# Volatility
Indicator.atr(period=14)
Indicator.atr_quantile(period=14, quantile=0.5, window=252)
Indicator.bollinger_bands(period=20, num_std=2.0)

# Trend
Indicator.supertrend(multiplier=3.0, volatility_column="ATR|14")
Indicator.macd(fast=12, slow=26, signal=9)
Indicator.adx(period=14)

# Momentum
Indicator.rsi(period=14)
Indicator.cci(period=20)
Indicator.stochastic(k_period=14, d_period=3)

# Volume Profile
Indicator.market_profile(
    period=30,
    tick_size=0.25,
    value_area_pct=0.70,
)

# Raw OHLCV
Indicator.raw_ohlcv(column="close")

---

Usage Examples

Multi-Strategy Backtesting

from tradepose_models.strategy import StrategyConfig, StrategyRegistry
from tradepose_client import BatchTester
from tradepose_client.batch import Period

# Setup registry
registry = StrategyRegistry()

# Load multiple strategies
for path in ["strat1.json", "strat2.json", "strat3.json"]:
    strategy = StrategyConfig.load(path)
    registry.add(strategy)

print(f"Registry: {len(registry)} entries")
# Registry: 8 entries (if strategies have multiple blueprints)

# Backtest all
configs = registry.get_configs()
tester = BatchTester(api_key="tp_xxx")
batch = tester.submit(
    strategies=configs,
    periods=[Period.Q1(2024), Period.Q2(2024)],
)
batch.wait()
print(batch.summary())

Portfolio-Based Testing

# Create focused portfolio
momentum_portfolio = registry.select(
    name="Momentum_Suite",
    selections=[
        ("TrendFollower", "momentum_long"),
        ("TrendFollower", "momentum_short"),
        ("BreakoutTrader", "range_breakout"),
    ],
    capital=50000,
    currency="USD",
)

# Test portfolio only
configs = momentum_portfolio.get_configs(registry)
batch = tester.submit(strategies=configs, periods=[Period.Q1(2024)])

# Save portfolio for later
momentum_portfolio.save("momentum_portfolio.json")

Indicator with Struct Fields

from tradepose_models.strategy import IndicatorSpec, Trigger
from tradepose_models.indicators import Indicator
from tradepose_models.enums import Freq, OrderStrategy
import polars as pl

# SuperTrend returns struct with multiple fields
supertrend = IndicatorSpec(
    freq=Freq.DAY_1,
    shift=1,
    indicator=Indicator.supertrend(multiplier=3.0, volatility_column="ATR|14"),
)

# Access struct fields in conditions
trigger = Trigger(
    name="supertrend_long",
    conditions=[
        supertrend.col().struct.field("direction") == 1,  # Long signal
        supertrend.col().struct.field("supertrend") < pl.col("close"),
    ],
    price_expr=pl.col("close"),
    order_strategy=OrderStrategy.IMMEDIATE_ENTRY,
    priority=1,
)

---

File Structure

packages/models/src/tradepose_models/
├── __init__.py
├── base.py                 # BaseModel configuration
├── enums/
│   ├── __init__.py
│   ├── freq.py             # Time frequencies
│   ├── trade_direction.py  # LONG/SHORT/BOTH
│   ├── trend_type.py       # TREND/RANGE/REVERSAL
│   ├── order_strategy.py   # Entry/exit strategies
│   ├── currency.py         # Currency types
│   ├── platform.py         # Trading platforms
│   ├── account_source.py   # Brokers/prop firms
│   └── ...
├── strategy/
│   ├── __init__.py
│   ├── config.py           # StrategyConfig
│   ├── blueprint.py        # Blueprint
│   ├── trigger.py          # Trigger
│   ├── indicator_spec.py   # IndicatorSpec
│   ├── registry.py         # StrategyRegistry, RegistryEntry, BlueprintSelection
│   ├── portfolio.py        # Portfolio
│   └── helpers.py          # Factory functions
├── indicators/
│   ├── __init__.py
│   ├── factory.py          # Indicator factory
│   └── ...                 # Indicator type definitions
├── schemas/
│   ├── __init__.py
│   ├── trades.py           # Trades schema
│   ├── performance.py      # Performance schema
│   └── ohlcv.py            # OHLCV schema
└── ...

---

Development

Running Tests

# Run models tests
uv run python -m pytest tests/models/ -v

# Run with coverage
uv run python -m pytest tests/models/ --cov=tradepose_models

---

License

MIT License - see LICENSE file for details.

---

Support

• Documentation: docs/
• Issues: GitHub Issues