--- name: investment-analyst description: Comprehensive investment analysis for Dr. Wael — Turkish funds (Kuveyt Türk Portföy), forecasting with Prophet/ARIMA, portfolio optimization (PyPortfolioOpt), economic data (TCMB/World Bank/FRED/IMF), real estate (Avcılar tracking), crypto (CoinGecko), global markets (yfinance). Use for any investment decision, fund tracking, forecasting, or portfolio rebalancing. ---

Investment Analyst Skill

محلل استثماري متكامل لـ د. وائل القيشاوي. يجمع بين:

• 🏦 Kuveyt Türk Portföy live NAV tracking (9 صناديق)
• 🇹🇷 بيانات الاقتصاد التركي (TCMB)
• 🌍 بيانات اقتصاد عالمي (FRED, World Bank, IMF, OECD)
• 📈 forecasting (Prophet, ARIMA, GARCH)
• 💼 portfolio optimization (PyPortfolioOpt - Markowitz)
• 🏠 Avcılar real estate (sahibinden + endeksa)
• 💰 crypto (CoinGecko) + global stocks (yfinance)

Tools Available

Python Libraries Installed

| المكتبة | الاستخدام | |---|---| | tefas | Kuveyt Türk + كل الصناديق التركية NAV | | prophet | time series forecasting (Facebook) | | pypfopt (PyPortfolioOpt) | Markowitz mean-variance optimization | | statsmodels | ARIMA, regression, econometrics | | arch | GARCH volatility models | | quantstats | performance analytics (Sharpe, Sortino, drawdown) | | yfinance | global stocks, FX, commodities, indices | | fmpsdk | Financial Modeling Prep (companies, earnings) | | ta | technical indicators (RSI, MACD, Bollinger) | | plotly | interactive charts |

Free APIs (no payment)

• TEFAS — صناديق تركيا (from tefas import Crawler)
• TCMB — البنك المركزي التركي (https://www.tcmb.gov.tr/kurlar/today.xml)
• World Bank — مؤشرات عالمية (https://api.worldbank.org/v2/)
• IMF DataMapper — GDP/forecasts (https://www.imf.org/external/datamapper/api/v1/)
• FRED — اقتصاد أمريكي (https://fred.stlouisfed.org/graph/fredgraph.csv?id=...)
• CoinGecko — كريبتو (https://api.coingecko.com/api/v3/)
• OECD — https://stats.oecd.org/SDMX-JSON/data/

Scripts Ready

• scripts/kt_funds_tracker.py — يجلب NAV حي + يحسب عائد المحفظة المرجّح

Quick Commands

الحصول على NAV حي لمحفظة د. وائل:

bash
python3 /data/.openclaw/workspace/scripts/kt_funds_tracker.py
Output: memory/kt-funds-latest.md + .json

Forecast صندوق معين بـ Prophet:

python
from tefas import Crawler
from prophet import Prophet
import pandas as pd
from datetime import date, timedelta
crawler = Crawler()
df = crawler.fetch(
    start=(date.today() - timedelta(days=730)).strftime("%Y-%m-%d"),
    end=date.today().strftime("%Y-%m-%d"),
    name="KPC"  # أو أي كود آخر
)
df = df.sort_values("date")
pf_df = pd.DataFrame({"ds": pd.to_datetime(df["date"]), "y": df["price"]})
m = Prophet(daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True)
m.fit(pf_df)
future = m.make_future_dataframe(periods=90)
forecast = m.predict(future)
print(forecast"ds","yhat","yhat_lower","yhat_upper".tail(10))

Portfolio Optimization (Markowitz):

python
import pandas as pd
from pypfopt import EfficientFrontier, risk_models, expected_returns
from tefas import Crawler
from datetime import date, timedelta
Collect historical prices for multiple funds
codes = ["KTV","KDE","KZL","KTM","KCV","KSR","KPC","KTJ","KGM"]
crawler = Crawler()
dfs = []
for c in codes:
    d = crawler.fetch(start=(date.today()-timedelta(days=730)).strftime("%Y-%m-%d"),
                     end=date.today().strftime("%Y-%m-%d"), name=c)
    dfs.append(d.set_index("date")["price"].rename(c))
prices = pd.concat(dfs, axis=1).dropna()
mu = expected_returns.mean_historical_return(prices)
S = risk_models.sample_cov(prices)
ef = EfficientFrontier(mu, S)
ef.add_constraint(lambda w: w >= 0.02)  # min 2% per fund
weights = ef.max_sharpe()  # or min_volatility()
cleaned = ef.clean_weights()
print(cleaned)
ef.portfolio_performance(verbose=True)

USD/TRY حي:

python
import requests, re
r = requests.get("https://www.tcmb.gov.tr/kurlar/today.xml", timeout=10)
m = re.search(r'Kod="USD".+?ForexBuying>([\d\.]+)<', r.text, re.S)
print(f"USD/TRY: {m.group(1)}")

تضخم تركيا تاريخي (World Bank):

python
import requests
r = requests.get("https://api.worldbank.org/v2/country/TR/indicator/FP.CPI.TOTL.ZG?format=json&date=2015:2024", timeout=10)
for item in r.json()[1]:
    print(f"{item['date']}: {item['value']}%")

Workflows

Workflow 1: Weekly Portfolio Health Check (الإثنين 08:00 الكويت)

1. Run kt_funds_tracker.py → latest NAV 2. Compare with last week's snapshot 3. Highlight changes > ±5% 4. Calculate USD-adjusted return using TCMB USD/TRY 5. Add to weekly_report output

Workflow 2: Forecast Before Investment

1. Get 2-year history of target fund via TEFAS 2. Run Prophet for 90-day forecast 3. Run ARIMA for comparison 4. Calculate GARCH volatility forecast 5. Compute Sharpe ratio (vs short-term sukuk benchmark KTV) 6. Decide: Buy / Hold / Skip

Workflow 3: Rebalancing Check (كل 3 شهور)

1. Get current NAVs + compute current portfolio weights 2. Compare with target weights 3. If any drift > 5% → suggest rebalance 4. Run PyPortfolioOpt optimization on latest data 5. Compare optimized weights with current target

Workflow 4: Real Estate Cross-Validation

1. Avcılar specific neighborhood data via OSM Overpass + sahibinden scraping 2. Compare apartment value trends with KT real estate funds (ZPV, OYZ) 3. Decide: hold apartments / diversify into funds / sell

Output Files Convention

| File | Content | |---|---| | memory/kt-funds-latest.md | Latest fund snapshot (markdown) | | memory/kt-funds-latest.json | Latest fund snapshot (structured) | | memory/kt-funds-history/YYYY-WW.json | Weekly snapshots (for trend analysis) | | projects/turkey-investment/forecasts/<CODE>-YYYYMMDD.md | Forecast reports per fund | | projects/turkey-investment/optimization-YYYYMMDD.md | Markowitz optimization results |

Cost Awareness

كل المكتبات والـ APIs المستخدمة في هذا الـ skill مجانية:

• TEFAS, TCMB, World Bank, IMF, FRED, CoinGecko: مجاني
• Prophet, PyPortfolioOpt, statsmodels: مفتوح المصدر
• yfinance: مجاني (لكن غير مضمون SLA)

استخدام أي مصدر مدفوع (Polygon, FMP Premium) يجب أن يستأذن أولاً.