Bottom Hunter Strategy

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

Pine ScriptMachine learning Breakout Mean reversion RSI MACD EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 65 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

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:

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

Risk Analysis

This strategy also has some risks:

The market may fall further without timely stop loss
The combination of multiple conditions to judge the bottom may miss the bottom in some scenarios
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:

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

Summary

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.

Parameters

Name	Default	What it controls
v_input_int_1	12	Fast Length
v_input_int_2	26	Slow Length
v_input_int_3	9	Signal Smoothing
v_input_int_4	14	RSI Length
v_input_int_5	20	BB Length
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_float_1	2	BB StdDev

Source Pine Script

/*backtest
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"}]
*/

//@version=5
strategy("MACD Divergence Strategy", shorttitle="Strategy: MACD Dive", overlay=true)

// MACDSettings
fastLength = input.int(12, "Fast Length")
slowLength = input.int(26, "Slow Length")
signalSmoothing = input.int(9, "Signal Smoothing")

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

// Calculation99dayEMAmoving average
ema99 = ta.ema(close, 99)

// CalculationRSI
rsiLength = input.int(14, title="RSI Length")
rsi = ta.rsi(close, rsiLength)

// Calculate the middle track of Bollinger Bands
length = input.int(20, "BB Length")
src = input(close, "Source")
mult = input.float(2.0, "BB StdDev")
basis = ta.sma(src, length)

// Buy filter conditions
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

// Define a variable to store the buying price
var float buyPrice = na

// Buy logic
if buySignal3
    buyPrice := close // Store the Price at Purchase
    strategy.entry("Buy", strategy.long)

// Take profit and stop loss conditions
longTakeProfit = buyPrice * 1.1 // Set take profit at 1.2 times the purchase price
longStopLoss = buyPrice * 0.98// Set stop loss to 0.99 times the purchase price

// Apply Take Profit and Stop Loss
strategy.exit("Exit", "Buy", limit=longTakeProfit, stop=longStopLoss)
// Draw buy signal
plotshape(series=buySignal3, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.triangleup, size=size.small)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-06. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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