Bottom Hunter Strategy

Author: ChaoZhang, Date: 2024-02-06 09:26:54



The Bottom Hunter strategy is a short-term trading strategy for cryptocurrencies. This strategy identifies appropriate entry points by recognizing bottoms during downtrends.

Strategy Principle

This strategy combines multiple technical indicators to identify bottoms. Specifically, it uses the MACD indicator to judge bottom reversal signals, the RSI indicator to determine oversold status, and Bollinger Bands to determine if the price is below the lower rail. A buy signal is generated when all conditions are met.

Firstly, the strategy uses MACD divergence to judge the bottom. So-called divergence means that the price makes a new low while the MACD indicator does not make a new low. This situation represents a weakening of trading volume and usually presages an impending trend reversal.

Secondly, the strategy requires that the RSI indicator is below 31.1. RSI below 30 represents an oversold state, which provides an opportunity to buy.

Finally, the strategy requires that the closing price is below the middle rail of the Bollinger Bands. This indicates that the price has fallen below the normal range, thus providing a better opportunity to buy.

When all the above conditions are met at the same time, the strategy generates a buy signal and establishes a position.

Advantage Analysis

The Bottom Hunter strategy has the following advantages:

  1. The use of multiple indicators to determine the bottom ensures the accuracy of bottom identification
  2. Utilizing MACD divergence to judge reversal signals is an experienced trading technique
  3. Judging both oversold and anomalies avoids the risk of false breakouts
  4. Conservative position control, only building positions at key points, avoids excessive trading

Risk Analysis

This strategy also has some risks:

  1. The market may fall further without timely stop loss
  2. The combination of multiple conditions to judge the bottom may miss the bottom in some scenarios
  3. Manual determination of parameters such as RSI thresholds may affect strategy performance

In response to the above risks, real-time tracking stop loss, adjusting parameter ranges, etc. can be used for optimization.

Optimization Directions

The strategy can be optimized in the following directions:

  1. Increase adaptive stop loss mechanism to flexibly adjust stop loss position based on market volatility
  2. Test and optimize the criteria for buy signal determination to identify optimal parameters
  3. Increase machine learning algorithms to automatically identify parameters and trading rules
  4. Add a trend judgment module to avoid entering consolidating markets during trending markets
  5. Incorporate additional indicators like volume change to improve bottom identification


The Bottom Hunter strategy buys on key bottoms in order to achieve excess returns. The rationale for determining the bottom is robust, while combining multiple filter conditions to avoid false signals. With proper parameter tuning and stop loss control, this strategy can perform well in short-term cryptocurrency trading.

start: 2023-01-30 00:00:00
end: 2024-02-05 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]

strategy("MACD Divergence Strategy", shorttitle="Strategy: MACD Dive", overlay=true)

// MACD设置
fastLength =, "Fast Length")
slowLength =, "Slow Length")
signalSmoothing =, "Signal Smoothing")

[macdLine, signalLine, _] = ta.macd(close, fastLength, slowLength, signalSmoothing)

// 计算99日EMA均线
ema99 = ta.ema(close, 99)

// 计算RSI
rsiLength =, title="RSI Length")
rsi = ta.rsi(close, rsiLength)

// 计算布林带中轨
length =, "BB Length")
src = input(close, "Source")
mult = input.float(2.0, "BB StdDev")
basis = ta.sma(src, length)

// 买入筛选条件
priceLow = ta.lowest(low[1], 60)
macdLow = ta.lowest(macdLine[1], 60)
divergence = low < priceLow and macdLine > macdLow

allHighsBelowEma99 = true
for i = 0 to 14
    if high[i] > ema99
        allHighsBelowEma99 := false

rsiBelow = rsi < 31.1
priceDifference = (high - low) / low * 100

buySignal1 = divergence and allHighsBelowEma99 and rsiBelow
buySignal2 = high < ema99 and priceDifference >= 3 and close < open and high < basis 
buySignal3 = buySignal1 or buySignal2

// 定义一个变量来存储买入时的价格
var float buyPrice = na

// 买入逻辑
if buySignal3
    buyPrice := close // 存储买入时的价格
    strategy.entry("Buy", strategy.long)

// 止盈和止损条件
longTakeProfit = buyPrice * 1.1 // 止盈设为买入价格的1.2倍
longStopLoss = buyPrice * 0.98// 止损设为买入价格的0.99倍

// 应用止盈和止损
strategy.exit("Exit", "Buy", limit=longTakeProfit, stop=longStopLoss)
// 绘制买入信号
plotshape(series=buySignal3, title="Buy Signal", location=location.belowbar,, style=shape.triangleup, size=size.small)