ChaoZhang
本策略名称为“基于二项分布的价格极值回归策略”。该策略利用二项分布函数判断价格出现反转的概率,并设定双EMA均线策略产生交易信号。
策略的计算逻辑如下:
计算最近20根K线中收盘价上涨的数量,并统计过去100根K线中上涨周期所占比例p。
将上涨周期数量和概率p带入二项分布函数,计算出cumulative distribution function(CDF)。
对CDF分别计算10日和20日的EMA均线。当快线上穿慢线时,认为价格极值回归的概率较大,产生买入信号。
当快线下穿慢线时,价格可能处于短期高点,此时产生卖出信号。
该策略的优点是通过概率方法判断价格的极值回归时机。但参数需要根据市场调整,避免产生过多假信号。
总体来说,统计方法有助于客观发现价格行为规律。但最终仍需要交易者对市场保持敏锐判断力,妥善使用技术指标作为辅助工具。
策略源码
/*backtest
start: 2022-09-06 00:00:00
end: 2023-05-01 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © pieroliviermarquis
//@version=4
strategy("Binomial Strategy", overlay=false, default_qty_type= strategy.percent_of_equity, default_qty_value= 100, slippage=1, initial_capital= 10000, calc_on_every_tick=true)
factorial(length) =>
n = 1
if length != 0
for i = 1 to length
n := n * i
n
binomial_pdf(success, trials, p) =>
q = 1-p
coef = factorial(trials) / (factorial(trials-success) * factorial(success))
pdf = coef * pow(p, success) * pow(q, trials-success)
binomial_cdf(success, trials, p) =>
q = 1-p
cdf = 0.0
for i = 0 to success
cdf := cdf + binomial_pdf(i, trials, p)
up = close[0] > close[1] ? 1 : 0
//long-term probabilities
lt_lookback = 100
lt_up_bars = sum(up, lt_lookback)
prob = lt_up_bars/lt_lookback
//lookback for cdf
lookback = 20
up_bars = sum(up, lookback)
cdf = binomial_cdf(up_bars, lookback, prob)
//ema on cdf
ema1 = ema(cdf, 10)
ema2 = ema(cdf, 20)
plot(cdf*100)
plot(ema1*100, color=color.red)
plot(ema2*100, color=color.orange)
buy = ema1 > ema2
sell = ema1 < ema2
//////////////////////Bar Colors//////////////////
var color buy_or_sell = na
if buy == true
buy_or_sell := #3BB3E4
else if sell == true
buy_or_sell := #FF006E
barcolor(buy_or_sell)
///////////////////////////Orders////////////////
if buy
strategy.entry("Long", strategy.long, comment="")
if sell
strategy.close("Long", comment="Sell")