Meninjau kinerja dengan drawdown

Anda telah mengimplementasikan strategi berbasis sinyal menggunakan dua indikator moving average. Anda melakukan backtest menggunakan data harga historis saham Tesla dari 2019 hingga 2020, dan plot hasilnya ditampilkan di sebelah kanan. Kini Anda ingin mengevaluasi kinerja strategi, khususnya volatilitas sisi bawah, dengan meninjau hasil drawdown dari backtest.

Hasil backtest bt telah disediakan dalam bt_result dan siap digunakan.

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan Python

Petunjuk latihan

Dapatkan semua statistik backtest dari bt_result dan simpan dalam resInfo.
Ambil nilai rata-rata drawdown dari resInfo.
Ambil rata-rata jumlah hari drawdown dari resInfo.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Obtain all backtest stats
resInfo = ____

# Get the average drawdown
avg_drawdown = ____
print('Average drawdown: %.2f'% avg_drawdown)

# Get the average drawdown days
avg_drawdown_days = ____
print('Average drawdown days: %.0f'% avg_drawdown_days)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

What is financial trading, why do people trade, and what’s the difference between technical trading and value investing? This chapter answers all these questions and more. You’ll also learn useful tools to explore trading data, generate plots, and how to implement and backtest a simple trading strategy in Python.

Exercise 1: What is financial trading Exercise 2: The concept of trading Exercise 3: Plot a time series line chart Exercise 4: Plot a candlestick chart Exercise 5: Getting familiar with your trading data Exercise 6: Resample the data Exercise 7: Plot a return histogram Exercise 8: Calculate and plot SMAs Exercise 9: Financial trading with bt Exercise 10: The bt process Exercise 11: Define and backtest a simple strategy

Let's dive into the world of technical indicators—a useful tool for constructing trading signals and building strategies. You’ll get familiar with the three main indicator groups, including moving averages, ADX, RSI, and Bollinger Bands. By the end of this chapter, you’ll be able to calculate, plot, and understand the implications of indicators in Python.

Exercise 1: Trend indicator MAs Exercise 2: Calculate and plot two EMAs Exercise 3: SMA vs. EMA Exercise 4: Strength indicator: ADX Exercise 5: Understand the ADX Exercise 6: Calculate the ADX Exercise 7: Visualize the ADX Exercise 8: Momentum indicator: RSI Exercise 9: Understand the RSI Exercise 10: Calculate the RSI Exercise 11: Visualize the RSI Exercise 12: Volatility indicator: Bollinger Bands Exercise 13: Understand Bollinger Bands Exercise 14: Implement Bollinger Bands

You’re now ready to construct signals and use them to build trading strategies. You’ll get to know the two main styles of trading strategies: trend following and mean reversion. Working with real-life stock data, you’ll gain hands-on experience in implementing and backtesting these strategies and become more familiar with the concepts of strategy optimization and benchmarking.

Exercise 1: Trading signals Exercise 2: Understand trading signals Exercise 3: Build an SMA-based signal strategy Exercise 4: Build an EMA-based signal strategy Exercise 5: Trend-following strategies Exercise 6: Construct an EMA crossover signal Exercise 7: Build and backtest a trend-following strategy Exercise 8: Mean reversion strategy Exercise 9: Match the signals with the strategies Exercise 10: Construct an RSI based signal Exercise 11: Build and backtest a mean reversion strategy Exercise 12: Strategy optimization and benchmarking Exercise 13: Conduct a strategy optimization Exercise 14: Perform a strategy benchmarking

How is your trading strategy performing? Now it’s time to take a look at the detailed statistics of the strategy backtest result. You’ll gain knowledge of useful performance metrics, such as returns, drawdowns, Calmar ratio, Sharpe ratio, and Sortino ratio. You’ll then tie it all together by learning how to obtain these ratios from the backtest results and evaluate the strategy performance on a risk-adjusted basis.

Exercise 1: Analisis imbal hasil strategi Exercise 2: Meninjau hasil imbal hasil dari backtest Exercise 3: Plot histogram return dari suatu backtest Exercise 4: Bandingkan hasil return dari beberapa strategi Exercise 5: Drawdown Exercise 6: Meninjau kinerja dengan drawdown

Latihan Saat Ini

Exercise 7: Hitung dan tinjau rasio Calmar Exercise 8: Rasio Sharpe dan rasio Sortino Exercise 9: Evaluasi kinerja strategi dengan rasio Sharpe Exercise 10: Evaluasi kinerja strategi dengan rasio Sortino Exercise 11: Selamat!