Validating Strategies¶

# BAD: shift(-N) accesses future data
df['signal'] = df['close'].shift(-1) > df['close']  # Uses tomorrow's price!

# BAD: Global statistics include future data
df['zscore'] = (df['close'] - df['close'].mean()) / df['close'].std()

# BAD: iloc[-1] comparison
df['signal'] = df['close'].iloc[-1] > df['close'].iloc[-2]

# GOOD: shift(1) uses past data
df['signal'] = df['close'] > df['close'].shift(1)  # Compare to yesterday

# GOOD: Rolling statistics (look-back only)
df['zscore'] = (df['close'] - df['close'].rolling(20).mean()) / df['close'].rolling(20).std()

# GOOD: Use expanding window
df['cum_mean'] = df['close'].expanding().mean()

class MyStrategy(TradAIStrategy):
    # Required metadata
    STRATEGY_NAME = "MyStrategy"  # Must match class name
    VERSION = "1.0.0"             # Semantic version
    CATEGORY = "momentum"          # Valid category
    TIMEFRAME = "1h"               # Valid timeframe

    # Optional but recommended
    AUTHOR = "Your Name"
    DESCRIPTION = "Strategy description"

class MyStrategy(TradAIStrategy):
    # If you use EMA(50), you need at least 50 candles
    startup_candle_count = 50  # Validates this is sufficient

    def populate_indicators(self, dataframe, metadata):
        dataframe['ema50'] = ta.EMA(dataframe['close'], 50)
        return dataframe

# Unified validation entities
from tradai.strategy.validation_entities import (
    CheckSeverity,           # ERROR, WARNING, INFO
    ValidationIssue,         # Base issue class
    MetricValidationIssue,   # With actual_value, threshold, suggestion
)

# Preflight validation
from tradai.strategy.preflight import PreflightValidationService

service = PreflightValidationService()
result = service.validate(
    strategy_name="MomentumV2",
    symbols=["BTC/USDT:USDT"],
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 6, 1),
)

if not result.valid:
    for check in result.checks:
        if not check.passed:
            print(f"{check.severity}: {check.message}")

# Sanity checks
from tradai.strategy.sanity import SanityCheckService, SanityCheckResult

service = SanityCheckService()
result: SanityCheckResult = service.check(backtest_metrics)

# Use ValidationResultMixin properties
if result.has_errors:
    print(f"Blocking issues: {result.error_count}")
    for error in result.errors:
        print(f"  ERROR: {error.message}")

if result.has_warnings:
    print(f"Warnings: {result.warning_count}")
    for warning in result.warnings_only:
        print(f"  WARN: {warning.message}")
        if warning.suggestion:
            print(f"       → {warning.suggestion}")

# CI gate validation
from tradai.strategy.ci import CIBacktestGate, CIValidationResult

gate = CIBacktestGate(thresholds)
result: CIValidationResult = gate.validate(backtest_result)

# Same ValidationResultMixin properties available
print(f"Passed: {result.passed}")
print(f"Errors: {result.error_count}, Warnings: {result.warning_count}")

from tradai.strategy.dataframe_validators import (
    validate_dataframe,
    analyze_nan_indicators,
    DEFAULT_WARMUP_CANDLES,
)

# Validate required columns and minimum rows
validate_dataframe(
    dataframe=df,
    required_columns={"open", "high", "low", "close", "volume"},
    min_rows=200
)

# Analyze NaN in indicator columns (skipping warmup period)
analysis = analyze_nan_indicators(
    dataframe=df,
    warmup_candles=DEFAULT_WARMUP_CANDLES,  # 50 candles default
)

if analysis.has_nan_issues:
    print(f"Columns with NaN: {analysis.nan_column_names}")
    for col in analysis.get_columns_above_threshold(10.0):  # >10% NaN
        print(f"  {col.column}: {col.nan_percentage:.1f}% NaN")