thequantscience

Who invented the Dollar Cost Averaging (DCA) investment strategy?The concept of Dollar Cost Averaging (DCA) was formalized and popularized by economists and investors throughout the 20th century, particularly with the growth of the U.S. stock market. One of the first to promote this strategy was Benjamin Graham, considered the father of value investing and author of the famous book The Intelligent Investor (published in 1949). Graham highlighted how DCA could help reduce the risk of buying assets at excessively high prices and improve investor discipline.When and How Did Dollar Cost Averaging Originate?The concept of DCA began to take shape in the early decades of the 20th century when financial institutions introduced automatic purchase programs for savers. However, it gained popularity among retail investors in the 1950s and 1960s with the rise of mutual funds.OverviewThe core principle of DCA involves investing a fixed amount of money at regular intervals (e.g., every month. This approach allows investors to purchase more units when prices are low and fewer units when prices are high, thereby reducing the impact of market volatility.Why Was DCA Developed?The strategy was developed to address key challenges faced by investors, including:1. Reducing Market Timing RiskInvesting a fixed amount periodically eliminates the need to predict the perfect market entry point, reducing the risk of buying at peaks.2. Discipline and Financial PlanningDCA helps investors maintain financial discipline, making investments more consistent and predictable.3. Mitigating VolatilitySpreading trades over a long period reduces the impact of market fluctuations and minimizes the risk of experiencing a significant drop immediately after a large investment.4. Ease of ImplementationThe strategy is simple to apply and does not require constant market monitoring, making it accessible to all types of investors.Types of DCADollar Cost Averaging (DCA) is an investment strategy that can be implemented in two main ways:Time-Based DCA → Entries occur at regular intervals regardless of price.Price-Based DCA → Entries occur only when the price meets specific criteria.1. Time-Based DCAHow It Works: The investor buys a fixed amount of an asset at regular intervals (e.g., weekly, monthly). Entries occur regardless of market price.Example: An investor decides to buy $200 worth of Bitcoin every month, without worrying whether the price has gone up or down.2. Price-Based DCAHow It Works: Purchases occur only when the price drops below a predefined threshold. The investor sets price levels at which purchases will be executed (e.g., every -5%). This approach is more selective and allows for buying at a “discount” compared to the market trend.Example: An investor decides to buy $200 worth of Bitcoin only when the price drops by at least 5% compared to the last entry.Challenges and Limitations1. DCA May Reduce Profits in Bull MarketsIf the market is in an bullish trend, a single trade may be more profitable than spreading purchases over time or price dips.2. Does Not Fully Remove Loss RiskDCA helps mitigate volatility but does not protect against long-term bearish trends. If an asset continues to decline for an extended period, positions will accumulate at lower values with no guarantee of recovery.3. May Be Inefficient for Active InvestorsIf an investor has the skills to identify better entry points (e.g., using technical or macroeconomic analysis), DCA might be less effective. Those who can spot market opportunities may achieve a better average entry price than an automatic DCA approach.4. Does Not Take Full Advantage of Price DropsDCA does not allow aggressive buying during market dips since purchases are fixed at regular intervals. If the market temporarily crashes, an investor with available funds could benefit more by buying larger amounts at that moment.5. Higher Transaction CostsFrequent small investments can lead to higher trading fees, which may reduce net returns. This is especially relevant in markets with fixed commissions or high spreads.6. Risk of Overconfidence and False SecurityDCA is often seen as a “fail-proof” strategy, but it is not always effective. If an asset has weak fundamentals or belongs to a declining sector, DCA may only slow down losses rather than ensure future gains.7. Requires Discipline and PatienceDCA is only effective if applied consistently over a long period. Some investors may lose patience and leave the strategy at the wrong time, especially during market crashes.

What is a Buy&Sell Strategy?A Buy&Sell trading strategy involves buying and selling financial instruments with the goal of profiting from short- or medium-term price fluctuations. Traders who adopt this strategy typically take long positions, aiming for upward profit opportunities. This strategy involves opening only one trade at a time, unlike more complex strategies that may use multiple orders, hedging, or simultaneous long and short positions. Its management is simple, making it suitable for less experienced traders or those who prefer a more controlled approach.Typical Structure of a Buy&Sell StrategyA Buy&Sell strategy consists of two key elements:1) Entry Condition Entry conditions can be single or multiple, involving the use of one or more technical indicators such as RSI, SMA, EMA, Stochastic, Supertrend, etc. Classic examples include: Moving average crossover Resistance breakout Entry on RSI oversold conditions Bullish MACD crossover Retracement to the 50% or 61.8% Fibonacci levels Candlestick pattern signals2) Exit Condition The most common exit management methods for a long trade in a Buy&Sell strategy fall into three categories: Take Profit & Stop LossExit based on opposite entry conditionsPercentage on equityPractical Example of a Buy&Sell StrategyEntry Condition: Bearish RSI crossover below the 30 level (RSI oversold entry). Exit Conditions: Take profit, stop loss, or percentage-based exit on the opening price.

In this article, will explain how to develop a simple backtesting for a Buy&Sell trading strategy using Pine Script language and simple moving average (SMA).Strategy descriptionThe strategy illustrated works on price movements around the 200-period simple moving average (SMA). Open long positions when the price crossing-down and moves below the average. Close position when the price crossing-up and moves above the average. A single trade is opened at a time, using 5% of the total capital.Behind the codeNow let's try to break down the logic behind the strategy to provide a method for properly organizing the source code. In this specific example, we can identify three main actions:1) Data extrapolation2) Researching condition and data filtering3) Trading execution1. GENERAL PARAMETERS OF THE STRATEGYFirst define the general parameters of the script. Let's define the name.Pine Script®"Buy&Sell Strategy Template [The Quant Science]"Select whether to show the output on the chart or within a dashboard. In this example will show the output on the chart.Pine Script®overlay = trueSpecify that a percentage of the equity will be used for each trade.Pine Script®default_qty_type = strategy.percent_of_equitySpecify percentage quantity to be used for each trade. Will be 5%.Pine Script®default_qty_value = 5Choose the backtesting currency. Pine Script®currency = currency.EURChoose the capital portfolio amount.Pine Script®initial_capital = 10000Let's define percentage commissions.Pine Script®commission_type = strategy.commission.percentLet's set the commission at 0.07%.Pine Script®commission_value = 0.07Let's define a slippage of 3.Pine Script®slippage = 3Calculate data only when the price is closed, for more accurate output.Pine Script®process_orders_on_close = true2. DATA EXTRAPOLATIONIn this second step we extrapolate data from the historical series. Call the calculation of the simple moving average using close price and 200 period bars. Pine Script®sma = ta.sma(close, 200)3. DEFINITION OF TRADING CONDITIONSNow define the trading conditions.Pine Script®entry_condition = ta.crossunder(close, sma)The close condition involves a bullish crossing of the closing price with the average. Pine Script®exit_condition = ta.crossover(close, sma)4. TRADING EXECUTIONAt this step, our script will execute trades using the conditions described above. Pine Script®if (entry_condition==true and strategy.opentrades==0) strategy.entry(id = "Buy", direction = strategy.long, limit = close) if (exit_condition==true) strategy.exit(id = "Sell", from_entry = "Buy", limit = close)5. DESIGNIn this last step will draw the SMA indicator, representing it with a red line. Pine Script®plot(sma, title = "SMA", color = color.red)Complete code below.Pine Script®//@version=6 strategy( "Buy&Sell Strategy Template [The Quant Science]", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 5, currency = currency.EUR, initial_capital = 10000, commission_type = strategy.commission.percent, commission_value = 0.07, slippage = 3, process_orders_on_close = true ) sma = ta.sma(close, 200) entry_condition = ta.crossunder(close, sma) exit_condition = ta.crossover(close, sma) if (entry_condition==true and strategy.opentrades==0) strategy.entry(id = "Buy", direction = strategy.long, limit = close) if (exit_condition==true) strategy.exit(id = "Sell", from_entry = "Buy", limit = close) plot(sma, title = "SMA", color = color.red)Expand 19 linesThe completed script will display the moving average with open and close trading signals.IMPORTANT! Remember, this strategy was created for educational purposes only. Not use it in real trading.

What is a benchmark?A benchmark is an index or a basket of assets used to evaluate the performance of an investment portfolio In the context of portfolio analysis the benchmark serves as a point of comparison to determine whether a fund a strategy or an investment is performing better worse or in line with the reference market.In the current chart, Bitcoin (BTCUSDT) is displayed with a solid and larger blue line in relation to other cryptocurrencies for the current period.Benchmarks are essential tools for institutional and private investors as they allow measuring the effectiveness of asset allocation choices and risk management Additionally they help determine the added value of an active manager compared to a passive market replication strategy.Benchmark analysis example: TSLA - NDX Benchmark analysis example: TSLA - AAPL - NDX What is the purpose of a benchmarkThe use of a benchmark in portfolio analysis has several objectives1) Performance Evaluation: Provides a parameter to compare the portfolio's return against the market or other funds2) Risk Analysis: Allows comparing the volatility of the portfolio against that of the benchmark offering a measure of risk management3) Performance Attribution: Helps distinguish between returns derived from asset selection and those linked to market factors4) Expectation Management: Supports investors and managers in assessing whether a portfolio is meeting expected return objectives5) Strategy Control: If a portfolio deviates excessively from the benchmark it may signal the need to review the investment strategyHow to select an appropriate benchmark?The choice of the correct benchmark depends on several factors:1) Consistency with Portfolio Objective: The benchmark should reflect the market or sector in which the portfolio operates2) Representativeness of Portfolio Assets: The benchmark should have a composition similar to that of the portfolio to ensure a fair comparison3) Transparency and Data Availability: It must be easily accessible and calculated with clear and public methodologies4) Stability Over Time: A good benchmark should not be subject to frequent modifications to ensure reliable historical comparison5) Compatible Risk and Return: The benchmark should have a risk and return profile similar to that of the portfolioMost used benchmarksThere are different benchmarks based on asset type and reference market Here are some of the most common.EquitySP500 Representative index of the 500 largest US companies.MSCI World Includes companies from various developed countries ideal for global strategiesFTSEMIB Benchmark for the Italian stock marketNDX Represents the largest technology and growth companiesBondsBarclays Global Aggregate Bond Index Broad benchmark for the global bond marketJP Morgan Emerging Market Bond Index EMBI Benchmark for emerging market debt[*]BofA Merrill Lynch US High Yield Index Representative of the high-yield bond market junk bondsMixed or Balanced6040 Portfolio Benchmark 60 equities SP 500 and 40 bonds Bloomberg US Aggregate used to evaluate balanced portfoliosMorningstar Moderate Allocation Index Suitable for moderate-risk investment strategiesAlternativeHFRI Fund Weighted Composite Index Benchmark for hedge fundsGoldman Sachs Commodity Index GSCI Used for commodity-related strategiesBitcoin Index CoinDesk BPI Benchmark for cryptocurrenciesA reference benchmark is essential in portfolio analysis to measure performance manage risk and evaluate investment strategies The selection of an appropriate benchmark must be consistent with the strategy and market of the portfolio to ensure meaningful comparison.Understanding and correctly selecting the benchmark allows investors to optimize their decisions and improve long-term results.

In this guide we will explain how to connect and automate a Buy&Sell strategy with 3Commas using a Trading View indicator. This guide will enable you to create long strategies on all spot pairs available on 3Commas. In this example we will set up a Buy&Sell bot that will open the long position when the Tweezer Bottom - Bullish indicator signals the pattern. Then we will illustrate all the steps necessary to open the long position. The position will be closed using 3Commas take profit and stop loss.1) Choice of the technical indicator to be used.Trading View offers an extensive library with technical indicators developed by the in-house team. To access all available indicators, open the indicators dashboard (A) and click on the Technicals section (B). In this example we will choose an indicator in the Patterns section (C) called Tweezer Bottom - Bullish (D). https://www.tradingview.com/x/49jYJ6Xp/Remember that the choice of this indicator is purely random and is for educational purposes only, be sure to backtest and research before building any trading strategy.https://www.tradingview.com/x/ptDTc5tC/2) Creating a bot on 3Commas.Now go to 3Commas and create a new DCA bot. This bot will allow you to connect the indicator signal. Set up the Main Settings section. Name your bot (A), select your exchange (B), and bot type (C). https://www.tradingview.com/x/SqQCi8rZ/Select the ticker (A), set the type of strategy (B) and the capital to be used (C).https://www.tradingview.com/x/eB4MmM0i/In the Deal Start Condition section, open the drop-down menu and select 'Trading View custom signal'.https://www.tradingview.com/x/dsgn9OdF/Set the take profit.https://www.tradingview.com/x/l3zleX5T/Set the stop loss.https://www.tradingview.com/x/p5GFq56R/Configure the Safety Orders section for a Buy&Sell strategy. Set the value to zero within this section as shown in the screenshot. Set Max safety orders count and Max active safety orders count to zero.https://www.tradingview.com/x/Tk0o1HwW/Now that you have properly created and configured your bot, go inside your new bot's 3Commas dashboard, scroll down, and copy the 'Initial Start Deal Condition' message. 3) Trading View Connection - 3Commas.Come back to Trading View and create a new alert (A), select the indicator from the drop-down menu (B), then choose Once Per Bar Close (C), and finally create a name for your alert and enter the message you copied previously within the Message field (D). https://www.tradingview.com/x/ezDCmWPi/As a last step, go into Notifications, enable the web-hook url and enter 3Commas' web-hook: '3commas.io/trade_signal/trading_view'.https://www.tradingview.com/x/B0nVvuRV/Create your alert as a final step. You have now correctly created a new 3Commas Buy&Sell bot that will automatically open new orders when a new pattern is generated.

Welcome to this new tutorial that helps traders and investors better understand the powerful Pine Script programming language v5.In this tutorial, we will program together three Input variables:Color type Input: a color input is a parameter that allows specifying a custom color for the indicator or script. It can be used to set the color of lines, areas, texts, or other graphical components in the indicator.Float type Input: a float input is a parameter that allows specifying a floating-point numerical value for the indicator or script. It can be used to set parameters such as threshold levels, indicator sizes, or any other numerical value that requires decimal precision.Integer type Input:an integer input is a parameter that allows specifying an integer numerical value for the indicator or script. It can be used to set parameters such as moving average periods, length of a time interval, or any other integer numerical value. IMPORTANT: The code used in this tutorial has been created purely for educational purposes.Our indicator is a simple indicator that plots the close data of the underlying asset on the chart in a weighted manner. The displayed data is the sum of the close price plus 20%. The goal of the indicator is to provide a fully dynamic tool that can vary its parameters from the user interface and update automatically.Here is the complete code for this tutorial:Pine Script®//@version=5 indicator("Input Tutorial", overlay = false) pond = input.float(defval = 0.20, title = "Float", minval = 0.10, maxval = 1, step = 0.10) color_indicator = input.color(defval = color.red, title = "Color") data = close + (close * pond) linewidth_feature = input.int(defval = 1, title = "Integer", minval = 1, maxval = 10, step = 1) plot(close, color = color_indicator, linewidth = linewidth_feature)Expand 2 linesPine Script®//@version=5 Indicates the version of the Pine Script language used in the code.Pine Script®indicator("Input Tutorial", overlay = false) Set the name of the indicator as "Input Tutorial", and overlay=false indicates that the indicator should not overlap the main chart.Pine Script®pond = input.float(defval = 0.20, title = "Float", minval = 0.10, maxval = 1, step = 0.10)Create a float input called "pond" with a default value of 0.20. The input title is "Float", and the minimum value is 0.10, the maximum value is 1, and the step is 0.10.Pine Script®color_indicator = input.color(defval = color.red, title = "Color")Create a color input called "color_indicator" with a default value of red color. The input title is "Color".Pine Script®data = close + (close * pond) Calculate a new value "data" by adding the closing price value with the closing price multiplied by the "pond" input.Pine Script®linewidth_feature = input.int(defval = 1, title = "Integer", minval = 1, maxval = 10, step = 1)Create an integer input called "linewidth_feature" with a default value of 1. The input title is "Integer", and the minimum value is 1, the maximum value is 10, and the step is 1.Pine Script®plot(close, color = color_indicator, linewidth = linewidth_feature) Plot the chart of the closing value with the color specified by the "color_indicator" input and the line width specified by the "linewidth_feature" input.