Pengantar

Beberapa hari yang lalu, didapati bahawa output kurva keuntungan dan kerugian dari hasil backtest strategi FMZ agak mudah, jadi saya berfikir sama ada untuk mendapatkan data hasil pendapatan dan kemudian memprosesnya sendiri untuk mendapatkan laporan penilaian kurva modal yang lebih terperinci dan memaparkannya secara grafik.pyfolio.

Apakah pyfolio?

pyfolioadalah perpustakaan Python untuk prestasi portfolio kewangan dan analisis risiko yang dibangunkan oleh quantinc. Ia berfungsi dengan baik dengan Zipline perpustakaan backtest sumber terbuka. quantinc juga menyediakan perkhidmatan pengurusan komprehensif untuk profesional, termasukZipline, Alphalens, Pyfolio, FactSetdata, dan lain-lain.

Inti daripadapyfolioadalah apa yang dipanggil yang dipanggil lembaran air mata, yang terdiri daripada pelbagai grafik bebas yang memberikan gambaran komprehensif mengenai prestasi algoritma dagangan.

GitHub address: https://github.com/quantopian/pyfolio

Belajar menggunakan pyfolio

Oleh kerana terdapat sedikit bahan pembelajaran dalam talian untuk alat ini, ia mengambil masa yang lama bagi saya untuk menggunakannya dengan mudah.

PyFolioRujukan API:

https://www.quantopian.com/docs/api-reference/pyfolio-api-reference#pyfolio-api-reference

Berikut adalah pengenalan yang lebih terperinci untukpyfolioPlatform ini boleh digunakan untuk backtesting saham AS.pyfolioSaya hanya belajar secara kasar. Nampaknya fungsi lain agak kuat.

Pemasang pyfolio

Pemasanganpyfolioadalah agak mudah, hanya ikuti arahan di GitHub.

Hasil ujian belakang FMZ yang dipaparkan oleh pyfolio

Pertama, dapatkan data kurva modal backtest pada platform FMZ.

Klik butang di sebelah skrin penuh dalam gambar di atas dalam carta keuntungan dan kerugian terapung hasil backtest, dan kemudian pilih Muat turun CSV. Format data CSV yang diperoleh adalah seperti berikut (nama fail boleh diubah mengikut keperluan anda):

Jika anda ingin mempunyai penanda aras perbandingan untuk hasil analisis, anda juga perlu menyediakan data harian K-line sasaran dagangan. jika tidak ada data K-line, hanya data pendapatan juga boleh dianalisis, tetapi akan ada beberapa lagi penunjuk untuk hasil analisis data penanda aras, seperti: Alpha, Beta, dll. Kandungan berikut ditulis mengikut data K-line asas.

Kita boleh mendapatkan data K-line terus dari platform melalui persekitaran penyelidikan FMZ:

# Use the API provided by the FMZ research environment to obtain K-line data which equal to the revenue data
dfh = get_bars('bitfinex.btc_usd', '1d', start=str(startd), end=str(endd))

Selepas data disediakan, kita boleh memulakan pengekodan. kita perlu memproses data yang diperoleh untuk menjadikannya sesuai dengan struktur data yang diperlukan olehpyfolio, dan kemudian hubungicreate_returns_tear_sheetantara mukapyfoliountuk mengira dan mengeluarkan hasilnya.returns, benchmark_rets=Nonedanlive_start_date=Nonetiga parameter.

Peraturanreturnparameter adalah data pendapatan yang diperlukan;benchmark_retsadalah data penanda aras, ia tidak perlu;live_start_datelive_start_datetidak perlu.

Makna parameter ini adalah: bilakah andareturnsSebagai contoh, kami mempunyai sekumpulanreturnsDi atas, dengan andaian bahawa kita memulakan pasaran sebenar selepas 2019-12-01, dan yang sebelumnya adalah dalam pasaran simulasi atau hasil backtest, maka kita boleh menetapkannya seperti ini:live_start_date = '2019-12-01'.

Dengan menetapkan parameter, kita secara teori boleh menganalisis sama ada strategi kita telah overfit. Jika perbezaan antara dalaman dan luar sampel adalah besar, maka terdapat kebarangkalian tinggi bahawa ini adalah overfit.

Kita boleh melaksanakan fungsi analisis ini dalam persekitaran penyelidikan FMZ, atau kita boleh melaksanakan secara tempatan. berikut mengambil pelaksanaan dalam persekitaran penyelidikan FMZ sebagai contoh:

https://www.fmz.com/upload/asset/1379deaa35b22ee37de23.ipynb?name=%E5%88%A9%E7%94%A8pyfolio%E5%B7%A5%E5%85%B7%E8%AF%84%E4%BB%B7%E5%9B%9E%E6%B5%8B%E8%B5%84%E9%87%91%E6%9B%B2%E7%BA%BF(%E5%8E%9F%E5%88%9B).ipynb

# First, create a new "csv to py code.py" python file locally and copy the following code to generate the py code containing the CSV file of the fund curve downloaded from FMZ. Running the newly created py file locally will generate "chart_hex.py" file.

#!/usr/bin/python
# -*- coding: UTF-8 -*-
import binascii

# The file name can be customized as needed, this example uses the default file name
filename = 'chart.csv'
with open(filename, 'rb') as f:
    content = f.read()
# csv to py
wFile = open(filename.split('.')[0] + '_hex.py', "w")
wFile.write("hexstr = bytearray.fromhex('" +
            bytes.decode(binascii.hexlify(content))
            + "').decode()\nwFile = open('" + filename + "', 'w')\nwFile.write(hexstr)\nwFile.close()")
wFile.close()

# Open the "chart_hex.py" file generated above, copy all the contents and replace the following code blocks, and then run the following code blocks one by one to get the chart.csv file

hexstr = bytearray.fromhex('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').decode()
wFile = open('chart.csv', 'w')
wFile.write(hexstr)
wFile.close()

!ls -la

cat chart.csv

# Install pyfolio library in research environment

!pip3 install --user pyfolio

import pandas as pd
import sys
sys.path.append('/home/quant/.local/lib/python3.6/site-packages')
import pyfolio as pf
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from fmz import * # import all FMZ functions

# Read fund curve data, FMZ platform download, cumulative income data
df=pd.read_csv(filepath_or_buffer='chart.csv')
# Convert to date format
df['Date'] = pd.to_datetime(df['DateTime'],format='%Y-%m-%d %H:%M:%S')
# Get start and end time
startd = df.at[0,'Date']
endd = df.at[df.shape[0]-1,'Date']

# Read the target asset daily K-line data, and use it as the benchmark income data
# Use the API provided by the FMZ research environment to obtain K-line data equal to the revenue data
dfh = get_bars('bitfinex.btc_usd', '1d', start=str(startd), end=str(endd))
dfh=dfh[['close']]
# Calculate the daily rise and fall based on the closing price of k-line data
dfh['close_shift'] = dfh['close'].shift(1)
dfh = dfh.fillna(method='bfill') #  Look down for the nearest non-null value, fill the exact position with this value, full name "backward fill"
dfh['changeval']=dfh['close']-dfh['close_shift']
dfh['change']=dfh['changeval']/dfh['close_shift']
# Frequency changes keep 6 decimal places
dfh = dfh.round({'change': 6})

# Revenue data processing, the FMZ platform obtains the cumulative revenue, and converts it to the daily revenue change rate
df['return_shift'] = df['Floating Profit and Loss'].shift(1)
df['dayly']=df['Floating P&L']-df['return_shift']
chushizichan = 3 #  Initial asset value in FMZ backtest
df['returns'] = df['dayly']/(df['return_shift']+chushizichan)
df=df[['Date','Floating Profit and Loss','return_shift','dayly','returns']]
df = df.fillna(value=0.0)
df = df.round({'dayly': 3}) # retain three decimal places
df = df.round({'returns': 6})

# Convert pd.DataFrame to pd.Series required for pyfolio earnings
df['Date'] = pd.to_datetime(df['Date'])
df=df[['Date','returns']]
df.set_index('Date', inplace=True)
# Processed revenue data
returns = df['returns'].tz_localize('UTC')

# Convert pd.DataFrame to pd.Series required for pyfolio benchmark returns
dfh=dfh[['change']]
dfh = pd.Series(dfh['change'].values, index=dfh.index)
# Processed benchmark data
benchmark_rets = dfh

# The point in time when real-time trading begins after the strategy's backtest period.
live_start_date = '2020-02-01'

# Call pyfolio's API to calculate and output the fund curve analysis result graph
# "returns" Parameters are required, the remaining parameters can not be entered
pf.create_returns_tear_sheet(returns,benchmark_rets=benchmark_rets,live_start_date=live_start_date)

Hasil analisis output:

Tafsiran hasil

Terdapat banyak data output, kita perlu menenangkan diri dan belajar apa yang bermakna penunjuk ini. biar saya memperkenalkan beberapa daripadanya. selepas kita mencari pengenalan kepada penunjuk yang berkaitan dan memahami makna penunjuk, kita boleh mentafsirkan status strategi dagangan kita.

• Pengembalian tahunan

Kadar pulangan tahunan dikira dengan menukar kadar pulangan semasa ( kadar pulangan harian, kadar pulangan mingguan, kadar pulangan bulanan, dan lain-lain) menjadi kadar pulangan tahunan. Ini adalah kadar pulangan teori, bukan kadar pulangan yang telah dicapai. Kadar pulangan tahunan perlu dibezakan dari kadar pulangan tahunan. Kadar pulangan tahunan merujuk kepada kadar pulangan untuk satu tahun pelaksanaan strategi dan adalah kadar pulangan sebenar.

• Hasil kumulatif

Konsep yang paling mudah difahami adalah pulangan strategi, yang merupakan kadar perubahan jumlah aset dari awal hingga akhir strategi. Volatiliti Tahunan Kadar turun naik tahunan digunakan untuk mengukur risiko turun naik sasaran pelaburan.

• Nisbah Sharpe

Menggambarkan hasil yang berlebihan yang boleh diperoleh oleh strategi dengan jumlah risiko unit.

• Pengeluaran Maksimum

Menggambarkan kerugian terbesar strategi. pengeluaran maksimum biasanya lebih kecil, lebih baik.

• Nisbah Omega

Satu lagi penunjuk prestasi risiko-balasan. Kelebihan terbesarnya berbanding nisbah Sharpe adalah-dengan pembinaan-ia mempertimbangkan semua momen statistik, sementara nisbah Sharpe hanya mempertimbangkan dua saat pertama.

• Nisbah Sortino

Menjelaskan hasil yang berlebihan yang boleh diperoleh oleh strategi di bawah risiko penurunan unit.

• Nilai-Risiko Harian

Nilai harian pada risiko-Indikator risiko lain yang sangat popular. Dalam kes ini, ia bermaksud bahawa dalam 95% kes, kedudukan (portfolio) disimpan untuk hari lain, dan kerugian tidak akan melebihi 1.8%.

Rujukan:https://towardsdatascience.com/the-easiest-way-to-evaluate-the-performance-of-trading-strategies-in-python-4959fd798bb3

• Nisbah ekor

Pilih kuantil ke-95 dan ke-5 untuk pengedaran pulangan harian, dan kemudian membahagikan untuk mendapatkan nilai mutlak.

• Kestabilan

Ini dipanggil kestabilan. sebenarnya, ia sangat mudah, iaitu, berapa banyak peningkatan masa menjelaskan nilai bersih kumulatif, iaitu, r-kuadrat regresi. ini agak abstrak, mari kita jelaskan secara ringkas.

Rujukan:https://blog.csdn.net/qtlyx/article/details/88724236

Cadangan kecil

Diharapkan FMZ dapat meningkatkan fungsi penilaian kurva modal kaya, dan meningkatkan fungsi penyimpanan hasil backtest sejarah, sehingga dapat memaparkan hasil backtest dengan lebih mudah dan profesional, dan membantu anda membuat strategi yang lebih baik.