Dry Information Coin Selection System Coin Selection Factors
Python strategy by 陈皮.
The original spec has no written description. Read the source below.
Source Python
import numpy as np
from scipy.stats import norm
from sklearn import preprocessing
import json
#Calculate Volatility Factor Value
def GetAtrFactorService(records):
atrlength = 14
atrs = TA.ATR(records, atrlength)
acs = sorted(range(len(atrs)), key=lambda k: atrs[k])
ac = acs[-1]
arr_mean = np.mean(acs)
arr_std = np.std(acs,ddof=1)
p = norm.cdf(x=ac, loc=arr_mean, scale = arr_std)
#PThe larger the value, the greater the volatility
atrFactor = _N(p,3)
return atrFactor
#Calculate the behavior trace factor value of the organization
def GetITFactorService(records):
#Benford's law distribution frequency
PN = [301, 176, 125, 97, 79, 67, 58, 51, 46]
FN = [0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(len(records)):
valume = records[i]['Volume']*10000
strValume = str(valume)
num = strValume[0]
for j in range(len(FN)):
key = j + 1
if int(num) == key:
FN[j] += 1
if sum(FN) == 0:
FN = PN
X = 0
for i in range(len(PN)):
X += (FN[i] - PN[i])**2
ITFactor = X
#X The larger the value, the greater the deviation of transaction volume data from Benford's ideal distribution, and the more pronounced the traces of institutional behavior.
return ITFactor
#Calculate the price factor value
def GetPriceFactorService(records):
record = records[-1]
price = record["Close"]
PFactor = 1/price
#PFactorThe larger the value, the lower the price
return PFactor
#Standardization --The calculated factor values need to be standardized due to different magnitudes. Missing values and outliers are not handled for now.
def StandardizedService(factor):
# Standardization
factorArray = np.asarray(factor)
factorArray = preprocessing.scale(factorArray)
factor = factorArray.tolist()
ext.GetAtrFactorService = GetArtFactorService
ext.GetITFactorService = GetITFactorService
ext.GetPriceFactorService = GetPriceFactorService
ext.StandardizedService = StandardizedServicePublished by 陈皮 on the FMZ strategy square · last modified 2022-02-12. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.
Build this with AgenKit
A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.
/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Dry-Information-Coin-Selection-System-Coin-Selection-Factors.md- npx agenkit install engineering-kit in your project
- Paste the command into your agent and approve each phase
- Get tests, backtest, risk controls and a paper-trading switch
Understand it first
Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.