MERT KURTÇU

Economist and Financial Engineer, Specializing in Algorithmic and Quantitative Trading.

[MERT KURTÇU @krmertc]

PORTFOLIO PERFORMANCE
Portfolio Value : $ 22150.63
Net Cash Flow : $ 15434.22

Cumulative Return : 1266.03 %
Annualized Return : 64.54 %
Annualized Volatility : 47.56 %
Maximum Drawdown : -41.17 %

Sharpe Ratio : 1.25
Sortino Ratio : 3.40
Calmar Ratio : 1.57

[MERT KURTÇU @krmertc]

ASSET ALLOCATION

#ODAS :
Average Cost = 5.60 ₺
Amount = 80.0K

#SELVA :
Average Cost = 2.55 ₺
Amount = 60.0K

#TATGD :
Average Cost = 17.8O ₺
Amount = 2.4K

[MERT KURTÇU @krmertc]

#TheManWhoSolvedTheMarket
Legendary Investors: Annualized Returns

Jim Simons (Medallion): 39.1% (1988–2018)
George Soros (Quantum): 32% (1969–2000)
Steve Cohen (SAC): 30% (1992–2003)
Peter Lynch (Magellan): 29% (1977–1990)
Warren Buffett (Berkshire): 20.5% (1965–2018)

[MERT KURTÇU @krmertc]

If you're building a serious trading system, a walk-forward framework with realistic transaction costs and slippage, combined with Monte Carlo stress testing, is the gold standard.

[MERT KURTÇU @krmertc]

#AlgorithmicTrading (Ernest P. Chan)
Out-of-sample testing, cross-validation, and high Sharpe ratios are all good practices for reducing data-snooping bias, but none is more definitive than walk-forward testing.

[MERT KURTÇU @krmertc]

#QuantitativeTrading (Ernest P. Chan)
It is the leveraged return that matters in the end, not the nominal return of a trading strategy.

[MERT KURTÇU @krmertc]

#MachineLearningForAlgorithmicTrading
The competition of sophisticated investors in financial markets implies that making precise predictions to generate alpha requires superior information, either through access to better data, a superior ability to process it, or both.

[MERT KURTÇU @krmertc]

#PythonForAlgorithmicTrading (Yves Hilpisch)
Be aware of the fact that the algorithmic trading world in general is secretive and that almost everyone who is successful is naturally reluctant to share their secrets in order to protect their sources of success.