Finter Alpha Strategy Development

Develop quantitative trading alpha strategies using the Finter framework.

⚠️ CRITICAL: VERIFY BEFORE SAVING alpha.py

EVERY alpha.py MUST have these 3 things. Check before saving:

# ✅ CHECK 1: Class name is exactly "Alpha"
class Alpha(BaseAlpha):  # ❌ NOT MyAlpha, MomentumAlpha, etc.

# ✅ CHECK 2: Return ends with .shift(1).fillna(0)
return positions.shift(1).fillna(0)  # ❌ NOT .shift(1) alone

# ✅ CHECK 3: pct_change has fill_method=None
close.pct_change(20, fill_method=None)  # ❌ NOT pct_change(20)

Pre-save checklist:

• • class Alpha(BaseAlpha) - not any other name
• • return ....shift(1).fillna(0) - both shift AND fillna
• • All pct_change() calls have fill_method=None
• • Position constraint:
  • •Long-only: row_sum <= 1e8
  • •Long-short: abs_sum <= 1e8 (positive=Long, negative=Short)

⛔ NO SELF-FEEDBACK (CRITICAL)

Single pass only. Backtest → Report → DONE. Fund Manager decides next steps.

❌ FORBIDDEN: "Sharpe 0.8, let me try different parameters..." → Backtest again
✅ CORRECT: Explore → Implement → Backtest → Report → STOP

💰 TURNOVER CHECK (Before Backtest)

Check signal stability BEFORE full backtest.

# Quick turnover check on your signal
daily_changes = (signal.diff() != 0).sum(axis=1).mean()
print(f"Avg daily changes: {daily_changes:.0f} stocks")

If turnover seems high, DIAGNOSE first before applying any fix. → Read references/mental_models/signal_processing.md for decision framework.

DON'T default to any specific technique. Ask:

1. •Is the noise frequency >> signal frequency?
2. •Are extreme values informative or noise?
3. •Does the fix actually improve IC?

📋 Workflow

1. •Read Framework: references/framework.md (REQUIRED)
2. •Read Mental Models: references/mental_models/signal_processing.md (REQUIRED)
3. •Explore Data: Load data in Jupyter, check distributions
4. •Diagnose Signal Quality: Before implementing, ask the diagnostic questions
5. •Find Template: Copy from templates/examples/
6. •Implement & Backtest: Write Alpha class, run Simulator
7. •Save alpha.py: Use Write tool (NOT Jupyter)
8. •Finalize: Run ONE command to generate all outputs
9. •Report & STOP: Report results, let Fund Manager evaluate

🎯 Templates

Copy and modify templates - don't write from scratch!

⚡ Quick Backtest

from finter import BaseAlpha
from finter.backtest import Simulator

alpha = Alpha()
positions = alpha.get(20200101, 20241213)

sim = Simulator(market_type="kr_stock")
result = sim.run(position=positions)

print(f"Sharpe: {result.statistics['Sharpe Ratio']:.2f}")
print(f"Return: {result.statistics['Total Return (%)']:.1f}%")
print(f"MaxDD: {result.statistics['Max Drawdown (%)']:.1f}%")

⚠️ Simulator Result API (EXACT)

Available attributes:

result.statistics   # pd.Series - Sharpe Ratio, Total Return (%), Max Drawdown (%), etc.
result.summary      # pd.DataFrame - nav, daily_return, cost, slippage, target_turnover, aum

These do NOT exist (don't try):

result.plot()            # ❌ NO - use matplotlib with result.summary['nav']
result.cumulative_return # ❌ NO - use result.summary['nav']
result.daily_return      # ❌ NO - use result.summary['daily_return']
result.nav               # ❌ NO - use result.summary['nav']
result.performance()     # ❌ NO - use result.statistics
result.pnl               # ❌ NO - use result.summary['nav']

Turnover & Cost calculation:

# Annual turnover (target_turnover is daily ratio, 1.0 = 100% of AUM)
annual_turnover = result.summary['target_turnover'].mean() * 252

# Cost drag as % of AUM
total_cost = result.summary['cost'].sum() + result.summary['slippage'].sum()
cost_drag_pct = (total_cost / result.summary['aum'].mean()) * 100

🚀 FINAL STEP (ONE COMMAND)

After saving alpha.py, run finalize.py:

python .claude/skills/finter-alpha/scripts/finalize.py \
    --code alpha.py \
    --universe kr_stock \
    --title "Strategy Name" \
    --category momentum

What finalize.py does:

1. •Validates - class name, position format, path independence
2. •Backtests - runs Simulator, calculates metrics
3. •Generates - chart.png, info.json, CSV files

Output files:

• •backtest_summary.csv, backtest_stats.csv
• •chart.png
• •info.json

If validation fails, fix alpha.py and re-run.

Categories: momentum | value | quality | growth | size | low_vol | technical | macro | stat_arb | event | ml | composite

Universes: kr_stock | us_stock | us_etf | vn_stock | id_stock | btcusdt_spot_binance

📚 Documentation

MUST READ (Before Implementation)

Reference During Coding

⚡ Data Loading Reference

from finter.data import ContentFactory
from helpers import get_start_date  # Included in templates

# Load with buffer (rule: 2x lookback + 250 days)
cf = ContentFactory("kr_stock", get_start_date(start, 300), end)

# Discover data items
print(cf.search('volume').to_string())
cf.usage('price_close')

# Load data
close = cf.get_df("price_close")

See finter-data skill for detailed data loading guide.