Understanding Crypto Trading Bot Data
1. INTRODUCTION CRYPTO TRADING BOT DATA
For anyone stepping into the world of crypto trading bots, the foundation of a successful trading bot strategy is built on solid trading data. At Oxido Solutions, we specialize in fully automated trend-following trading bot systems. This means our Binance trading bot, Bybit trading bot and OKX trading bot are configured to take a position only during a strong trend in the cryptocurrency market. While there are several strategies like grid-trading, DCA bot trading, and more, aimed at tapping into inefficiencies on cryptocurrency platforms, the universal requirement remains: the critical need for accurate trading data. Whether it’s historical data for back-testing algorithmic strategies or real-time exchange information, the varieties of trading bot data are extensive. In this blog, we detail each type, serving as a guide for those shaping their trading strategies. We’ll also highlight how we use this data in our own strategies, offering insights for newcomers, experienced traders, and investors alike.
2. Key crypto trading data categories
Trading data is crucial to the optimal operation of any crypto trading bot system. An effective system that identifies valuable trading opportunities depends on access to top-notch data and the expertise to interpret it correctly. As technologies like AI trading and quantum computing evolve, the significance of data in the cryptocurrency market intensifies. At their core, cryptocurrency trading systems use two primary data types: quantitative data and qualitative data. Within these types, the data can be classified as either historical or real-time.
3. Quantitative crypto trading bot data
Quantitative trading bot data is primarily used by bots built on the principles of technical analysis (TA). TA is a method that predicts a crypto’s price movement based on historical data and various analytical tools. This data is also employed by crypto traders who use trading strategies that combine both TA and fundamental analysis (FA), which evaluates market-related factors such as inflation. Quantitative trading bot data is essential for crypto trading bot systems for several reasons:
A. Objective Analysis
Quantitative trading bot data provides clear, measurable information about the cryptocurrency market, free from human biases. Crypto bots rely on precise and consistent information to make decisions, and quantitative data provides that reliability.
B. Algorithmic Precision
Crypto trading bots operate using trading algorithms, which require specific numerical inputs to generate outputs. Quantitative data provides the exact numerical values these crypto algo’s need to function correctly and consistently.
C. Historical Backtesting
Before deploying a trading strategy in real-time, it’s common practice to test it thoroughly. This includes testing against historical data to see how it would have performed in the past. This process, known as backtesting, relies heavily on historical quantitative data.
D. Trend Analysis
Quantitative data, especially when collected over time, allows bots that utilize a trend following trading approach to identify and act upon market trends. For instance, a bot can calculate moving averages, momentum, or other statistical measures to determine potential future price movements.
E. Real-time Decision Making
Crypto markets are highly volatile and can change rapidly within short time frames. Quantitative real-time data feeds allow crypto bots to react instantly to market changes, optimizing for profits and minimizing losses.
F. Scalability
One of the main advantages of crypto trading bots is their capability to process vast amounts of data at an impressive speed, far surpassing human capabilities. With data sourced from multiple crypto exchanges or pairs, this swift analysis empowers trading bots to execute various strategies, effectively responding to even the smallest price movements in the dynamic crypto market.
G. Risk Management
Risk management is essential in crypto trading bot systems. As price swings can be swift and substantial, a trader’s capital can be quickly eroded without adequate risk controls. Quantitative data plays a crucial role in risk management by providing hard numbers on which risk assessment models can base their calculations. This ensures that cryptocurrency trading strategies remain within predefined risk parameters.
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3.1. Historical crypto price data
Historical crypto price data is one of the most used quantitative data types. It is a look back at the previous prices of a crypto asset, showcasing the asset’s price history over a certain period of time.
This crypto data typically includes the opening price (the price at the beginning of a given period), the closing price (the last trading price of that period), the highest price, and the lowest price during that period. This set of data is commonly known as OHLC data.
Understanding this historical data is essential for Bitcoin trading strategies and other cryptocurrency strategies. It helps traders and algorithms in spotting patterns and shifts in price moments. By studying how prices have behaved in the past, traders aim to forecast future price movements and make profitable trades accordingly.
Most crypto strategies depend on a detailed study of this past price data. They use tools like moving averages, trend lines, and other indicators to detect and track price trends. The belief here is that trends from the past, once established, tend to carry on in the same direction.
3.2. Real-time crypto price data
Real-time crypto price data refers to the live, up-to-the-moment prices of the particular cryptocurrency being traded on the market. It provides crypto trading bots the immediate information they need to execute a wide range of strategies effectively and profitably. Real-time crypto price data is vital for various reasons:
A. Rapid Market Fluctuations
Cryptocurrency markets often experience significant price movements in short periods. For trading bots to stay effective, they need to adapt to these quick changes. Using real-time price data ensures the bot has the most recent information, allowing it to make better-informed decisions.
B. Trend-following trading bot strategies
These strategies rely on capturing gains through the analysis of an asset’s momentum in a particular direction. Given the speed at which crypto prices can change, having real-time data is essential to identify and act upon ongoing trends before they shift.
C. HFT bot trading strategies
High frequency trading bots that operate at high frequencies require real-time data to exploit small price differences in fleeting timeframes, sometimes milliseconds.
D. Grid Trading bot Strategies
This approach involves placing buy and sell orders at regular intervals above and below a predefined base price. Real-time data ensures the bot knows the current price and can adjust its grid levels accordingly. This enhances its chances of benefiting from price fluctuations.
E. DCA bot Strategies
Dollar Cost Averaging (DCA) involves purchasing a fixed dollar amount of a cryptocurrency at regular intervals, irrespective of its price. Real-time price data helps DCA bots in determining the exact amount of cryptocurrency to be purchased at each interval.
F. Crypto Arbitrage Strategies
Crypto arbitrage bots looking to capitalize on price differences of an asset across different exchanges rely heavily on real-time data. Price discrepancies can be fleeting, and acting even seconds late can render a potential arbitrage opportunity unprofitable.
G. AI trading bot strategies
AI trading bots adjust their strategies based on prevailing market conditions. Without the latest data, these algorithms won’t be able to tweak their actions effectively.
H. Automated Stop-Loss and Take-Profit
These mechanisms are tools used by algorithmic traders to automatically close a trade at a pre-defined price level, either to protect capital or lock in profits. The Stop-Loss is set to limit potential losses. When the cryptocurrency’s price reaches this predetermined level, the trading bot will automatically produce a sell alert to prevent further losses. Conversely, the Take-Profit is set to lock in profits. When the asset’s price hits this pre-set level, the crypto bot generates a sell alert to secure the gains.
For these automated mechanisms to function effectively, they need accurate and immediate price data. The real-time price data ensures that the Stop-Loss and Take-Profit triggers are based on the most current market conditions. If there’s a sudden drop or spike in the crypto’s price, the real-time data will instantly be processed by the trading bot, and if the price aligns with the pre-set Stop-Loss or Take-Profit values, the trade will be executed immediately.
Real-time data enables crypto trading bots to immediately create buy and sell opportunities when specific price levels are attained, enhancing the chances of minimizing losses and maximizing profits.
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3.3. Historical trading volume
Historical trading volume refers to the recorded past quantity of a crypto asset that has been traded over a specific period of time. This data includes the number of coins or crypto derivatives contracts that have been bought and sold, providing valuable insight into the cryptot’s liquidity and investor interest.
Within a trading bot system, historical trading volume has several key roles:
A. Trend Confirmation
Price direction mainly determines a trend, but volume can help confirm it. Typically, if crypto trading volume increases in the trend’s direction (more volume during an uptrend, less during a downtrend), it strongly confirms the trend. If volume doesn’t back the price trend, it might hint at a weakening trend.
B. Market Strength
You can use historical trading volume to analyze the strength or weakness of the cryptocurrency market. High trading volume shows strong interest and a vibrant market, possibly leading to bigger price changes and more trading chances. In contrast, low volumes might point to limited interest or a less active crypto market.
C. Liquidity Check
Cryptocurrency trading volume can show how liquid the crypto market or a specific asset is. Lots of trading usually means high liquidity, letting crypto bot traders buy and sell without major price shifts.
3.4. Real-time trading volume
Real-time trading volume refers to the live, up-to-the-minute measure of the total quantity of a specific crypto asset being bought and sold during a specific trading session or time interval. This live data provides instant insights into the current trading activity and market liquidity for that specific cryptocurrency.
Real-time trading volume has several important functions:
A. Instant Trend Confirmation
Like with historical trading volume, real-time volume can help confirm the validity of a trend.If volume rises with a price change, it usually indicates a robust trend. But, if the trend comes with a drop in volume, it might mean the trend is weakening.
B. Immediate Market Strength Check
Real-time trading volume lets cryptocurrency traders swiftly analyze the current market strength or weakness. When trading volumes are high, it usually indicates strong market activity and could lead to larger price movements. On the other hand, low volumes might hint at limited market action right now.
C. Current Liquidity Assessment
Using real-time volume data, crypto traders can get a sense of the market’s current liquidity. Cryptocurrencies with high trading volumes, like Bitcoin and Ethereum, tend to be more liquid. This means you can buy or sell them more smoothly with minimal effect on their price.
D. Risk Management
Checking real-time trading volumes helps traders decide on the risks they’re comfortable with. If volume is too low, they might hesitate to jump in, wary of potential price swings or unexpected changes. In contrast, higher volumes can indicate good liquidity and less chance of sudden price shifts.
In a nutshell, real-time trading volume plays a vital role in crypto bot trading systems. It helps with quick trend spotting, fast market strength checks, understanding current liquidity, and managing risks wisely.
3.5. Crypto Order book data
Order book data refers to a real-time, constantly updated list of buy and sell orders for a certain cryptocurrency. Each order within the order book specifies the quantity being bid (buy orders) or offered (sell orders) and the price the buyer or seller is willing to accept.
In crypto bot trading systems, order book data serves several key purposes:
A. Market Depth insight
Crypto order book data provides insight into the market depth, indicating the cryptocurrency market’s ability to handle sizable orders without impacting the price of a crypto. By analyzing this, traders can understand how robust or weak a trend is.
B. Price Discovery: It helps in price discovery as it highlights where demand (buy orders) and supply (sell orders) for a given asset is located. Using this data, crypto traders can predict potential future price movements and thus adjust their trades accordingly.
C. Liquidity Assessment: Order book data gives an indication of liquidity. A ‘thick’ order book with many orders close to the current price usually signifies high liquidity, ensuring trades can be executed without significant price slippage. On the other hand, a ‘thin’ order book might hint at lower liquidity and a higher risk of slippage.
D. Sentiment Analysis
By examining the balance between buy and sell orders, crypto traders can get a sense of market sentiment. A predominance of one type might suggest the direction in which the market is leaning.
It’s essential to understand that while crypto order book data offers insightful information, it has its constraints when applied to trading bot strategies. Order book data can be inherently “noisy” and shifts quickly, making it challenging to integrate into crypto bots operating on high time frames. Moreover, it’s vulnerable to manipulative tactics like ‘spoofing’, where significant orders are placed to give a deceptive perception of demand or supply, only to be withdrawn later.
Despite these challenges, when utilized judiciously, order book data can bolster the effectiveness of a crypto trading bot system, offering real-time insights into market depth, price levels, liquidity, and trader sentiment.
3.6. Volatility data
In the world of crypto trading bots, volatility data is crucial, directly affecting the bot’s performance. Volatility is a statistical measure that shows the distribution of returns for a specific asset or market index. It’s often measured by the standard deviation or variance between returns of that asset or index. In basic terms, volatility signifies how large the price changes are for a particular asset.
Volatility data can be pivotal for cryptocurrency trading systems for several reasons:
A. Trend Identification
High volatility often indicates new trends. When a crypto’s price moves with increased volatility, it might hint at the beginning of a new trend. This would be a signal for a crypto trading bot system to potentially open a new position.
B. Position Sizing
Volatility is commonly used in determining the size of the position that a crypto bot might take on a given trade. Typically, greater volatility results in smaller position sizes to account for the heightened risk.
C. Risk Management
Volatility is a key element in risk management. Cryptocurrency bots, such as those for Binance and OKX, must consider an asset’s volatility when determining stop losses and take profit points. In general, with increased volatility, these thresholds should be set wider to avoid being stopped out by normal market noise.
D. Trading Strategy Adjustment
The strategy of a crypto trading bot might need to be adjusted based on volatility levels. For instance, during periods of low volatility, a bot might adopt a more patient approach, holding onto positions for longer durations to realize potential profit from price shifts.
Remember, high volatility can present both opportunities for profit from significant price movements and risks, as prices can move against your position with equal speed.Thus, understanding and properly managing volatility is crucial for the succes of a crypto trading system.
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3.7. Cryptocurrency Trading fees
The data on cryptocurrency trading fees outlines the expenses traders face when making transactions on a digital currency exchange. It’s important to understand these fees when incorporating them into a crypto trading bot system, as they have a big impact on the overall trading profitability. This is because these systems often rely on making many trades that aim to capture small, consistent profits. High trading fees can significantly wipe out these small profits. Reliable crypto fees data can help traders choose the most cost-effective platforms and adjust their strategies to minimize trading costs.
Different crypto trading products, such as Spot and Derivatives, have distinct fee structures due to their unique design. For instance, crypto spot trading fees relate to the costs associated with direct purchases or sales of cryptocurrencies in real-time markets. A defining feature of spot trading is that traders gain ownership of the actual cryptocurrency they buy. This method limits profit opportunities to upward market movements and doesn’t support leveraged trades.
In contrast, crypto derivatives trading fees arise from contract-based trading. In this setup, traders don’t necessarily own the cryptocurrency but hold a stake in the contract’s underlying value. These contracts could be either long or short, allowing traders to potentially benefit from both upward and downward price fluctuations of the associated crypto asset. Additionally, derivatives trading supports leveraged positions.
In the following sections, we’ll delve into the specific fees associated with both crypto spot and derivatives trading.
3.7.1. Trading fees spot
Here in a overview of the trading fees on crypto spot pairs:
A. Transaction Costs
Every time a trade is placed, whether it is a buy or sell order, there may be a transaction fee associated with it. These fees are usually a small percentage of the total trade value, and they are charged by the cryptocurrency exchange where the trade is executed. It’s essential to account for these fees when calculating the potential profit or loss of a trade.
B. Slippage Costs
Slippage occurs when the price at which a trade is executed does not match the expected price. This discrepancy is usually due to market volatility or low liquidity. Slippage can be seen as an additional cost because it can reduce the profitability of trades.
C. Deposit and Withdrawal Fees
Exchanges may charge a fee to deposit and withdraw your funds. This fee can vary depending on the type of crypto asset and the current state of the blockchain network.
3.7.2. Trading fees crypto derivatives
Trading cryptocurrency derivatives products like bitcoin perpetual futures also involves trading fees, but the structure of these fees can be somewhat different compared to crypto spot. Here are some of the key considerations:
A. Maker and Taker Fees
Crypto derivatives exchanges typically charge different fees depending on whether you’re a “maker” or a “taker”. A maker is someone who adds liquidity to the market by placing a limit order that isn’t immediately filled. A taker removes liquidity by placing an order that gets filled right away (like a market order). Makers usually pay lower fees than takers because they help to increase the exchange’s trading volume and liquidity.
B. Funding Rate
In perpetual futures contracts, there’s an additional fee called the “funding rate”. Unlike traditional futures contracts, perpetual futures don’t have an expiry date. To keep the price of the perpetual future contract in line with the underlying asset’s spot price, exchanges use a mechanism called “funding”. If the funding rate is positive, long positions pay short positions. In contrast, if the funding rate is negative, short positions pay long positions. The funding rate is typically updated every few hours.
C. Liquidation Fees
If a crypto trader’s position gets liquidated because they couldn’t meet the margin requirements, some exchanges may charge a liquidation fee.
When trading cryptocurrency derivatives, it’s crucial to be aware of all these fees, as they can significantly affect your profitability, especially in a trading bot system where multiple positions may be opened and closed over a relatively short period.
BYBIT FEES
3.8. Technical indicators data
Technical indicators data refer to the information used in statistical analysis techniques by crypto traders to predict future price movements based on historical prices and volume data. These are mathematical calculations visually displayed on most charting platforms and often serve as the basis for trading decisions in a crypto trading bot system.
Below are some categories of technical indicators that trading bot systems might employ:
A. Trend Indicators
As the name suggests, trend indicators help crypto traders identify the presence and direction of a trend. The most commonly used trend indicators include Moving Averages (MA), Moving Average Convergence Divergence (MACD), and the Average Directional Index (ADX).
B. Momentum Indicators
These indicators help traders to capture the strength or speed at which the price of an asset is moving. Examples include the Relative Strength Index (RSI), Stochastic Oscillator, and Rate of Change (ROC).
C. Volatility Indicators
Volatility indicators measure the rate of price movements, regardless of the direction. They help crypto traders to understand the dynamics of price fluctuations. Examples include Bollinger Bands and the Average True Range (ATR).
D. Volume Indicators
Volume indicators are used to understand the strength of price movements. High volume often signals strong price moves and is seen as a confirmation of the trend. Examples include the On Balance Volume (OBV) and Chaikin Money Flow (CMF).
The particular technical indicators used by a crypto trading bot system will vary based on the strategy’s parameters. For instance, a system might use moving averages to determine the trend’s direction and then use volume indicators to confirm the trend’s strength.
It’s crucial to understand that while technical indicators offer valuable insights, they aren’t foolproof. They should be combined with other data points and risk management approaches. No single indicator can forecast market movements with absolute certainty, and at times, different indicators might present conflicting signals. Therefore, crypto traders often incorporate multiple indicators, seeking consistency in their signals to enhance the likelihood of successful trades.
E. Fundamental data
Fundamental quantitative data refers to the numerical data related to the financial and economic aspects of a traded asset. Though most crypto trading bot systems mainly rely on technical data (price and volume), they may also integrate specific fundamental quantitative data to enhance the trading signals and reduce risks.
Below are some examples of fundamental quantitative data:
F. Financial Statements
For cryptocurrency projects or platforms, these might include the project’s token allocation, funds raised during Initial Coin Offerings (ICOs) or token sales, usage metrics of their platform, and transparency reports. These data points give traders insights into a project’s sustainability and financial health, which could indirectly influence its token or coin price trends.
G. Economic Indicators
These include macroeconomic data such as GDP, employment figures, and inflation rates. Crypto traders might consider these, especially when analyzing the potential long-term value or stability of a cryptocurrency tied to a specific economy or when deciding on crypto-assets that have a strong correlation with traditional markets.
H. Market Data
This includes data like token market capitalization, token distribution, circulation supply, and other such metrics. For example, a significant change in a cryptocurrency’s market capitalization can signal a trend shift.
I. Token Performance Reports
These are particularly important for cryptocurrencies. A performance report or project update that significantly exceeds or misses community expectations can result in a strong price trend.
J. Crypto News and Updates
While not numerical, significant news and updates related to the cryptocurrency world are often quantified and incorporated into algorithmic trading systems. This could include regulatory changes, security breaches, blockchain upgrades, partnership announcements, and more.
Incorporating fundamental quantitative data into a crypto trading bot system can help traders identify when the broader crypto market or project-specific conditions might be changing in a way that could impact prevailing trends. While a purely technical crypto trading bot might overlook these fundamental aspects, some traders believe that monitoring these factors can provide additional insights and potentially offer a competitive edge.
Remember, however, that crypto trading bot systems primarily depend on technical analysis. Fundamental indicators are generally used to inform and reinforce the trading signals provided by the technical analysis, not to replace it. Fundamental data tends to be more useful for longer-term trend analysis rather than short-term trading signals, as it often takes time for changes in fundamental data to be reflected in asset prices.
4. Qualitative crypto trading bot data
Compared to quantitative data, qualitative data in the crypto realm is less structured and includes information that is more subjective. Many crypto trading bot systems might not prioritize qualitative data, but here’s why it can be valuable:
A. Comprehensive Understanding
While quantitative data gives numbers and patterns, qualitative data offers insights into why certain market behaviors occur. This can provide context to the numerical data and create a fuller understanding of market dynamics.
B. Sentiment Analysis
Qualitative data can help in gauging market sentiment, which is the collective mood or opinion of traders and investors about a particular cryptocurrency. This can be crucial for predicting sudden shifts in market trends influenced by news events, regulatory changes, or other external factors.
C. Diversification of Strategy
A crypto trading bot that incorporates qualitative data can adjust its strategy based on diverse data inputs. For instance, while quantitative data might signal a sell, qualitative data about an upcoming positive event for a cryptocurrency might prompt the bot to hold or buy.
D. Improved Decision-making
By including qualitative data, crypto trading bot systems can make more informed decisions, considering a broader range of factors that might not be immediately apparent in raw numbers alone.
E. Adaptability
Markets can be influenced by non-quantifiable events such as political decisions, regulatory changes, technological innovations, or even rumors. Qualitative data helps crypto bots to navigate these unexpected events and adjust their strategies as needed.
F. Risk Management
Understanding the motivations behind market movements enables crypto bots to better assess potential risks and rewards, refining their trading approaches to safeguard against losses.
G. Competitive Advantage
As most trading bots focus heavily on quantitative data, having a system that also understands and uses qualitative data can provide a significant edge over competitors.
H. Augment Quantitative Data
Qualitative data can help to understand the reasons behind the numbers, i.e., why a particular trend is happening. It can add depth to the quantitative analysis and provide a more holistic view of the market dynamics.
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5. How Oxido Solution uses trading data
5.1. Our strategy
Oxido Solutions uses its proprietary Ultimate Trading Bot System©️ to execute its trend-following trading strategy. This strategy seeks to take advantage of volatility in the crypto derivatives market. It identief buying and selling opportunities for the Bitcoin Perpetual Futures trading pair on leading crypto exchanges. To execute our strategy, we exclusively use the following quantitative data sources.
5.2. TradingView
TradingView is an online platform tailored for traders and investors. Oxido Solutions taps into it not solely for its charting capabilities but also for the essential trading data it offers, coupled with the capability to create trading bot strategies.
5.2.1. Historical data
Given that TradingView supplies historical price data, volatility data, and volume data of Bitcoin from 2013 up to the present, we’ve been able to backtest our trading strategy over an extended period across multiple crypto exchanges.
5.3. Indicator data
Within TradingView, Oxido Solutions has created three indicators by using the technical indicator data available:
A. Range Maker
In 2017, Oxido Solutions created the Range Maker indicator, our initial trend-following trading algorithm, which was made available to the public in 2019. Instead of using standard methods like Bollinger Bands to set the trading range, we took a unique approach. Our way of measuring the trading range, combined with custom features, ensures consistent performance and lowers the risk of market manipulation.
B. ATR
In 2020, we launched another cryptocurrency strategy indicator: the Average True Range (ATR). The ATR algorithm is designed to respond quickly to minor trend changes, working best over shorter durations. By pairing the ATR with the Range Maker, we enhance the monthly returns of our strategy, as they complement each other. This combined method allows our trading bot to adjust more effectively to changing market conditions.
C. Sideways filter
One of the biggest challenges with trend-following trading bot strategies is their suboptimal performance during weak trends. Our solution is the sideways filter. This efficient technical indicator identifies sideways price movements early on. As a result, our trading system avoids trades during weak trends and only becomes active when there’s a strong trend. This approach reduces potential drawdowns and optimizes profits.
5.4. Exchanges
Trading alerts generated from our strategies in TradingView are seamlessly sent to our middleware, Alpha Shifter. Alpha Shifter, in turn, forwards these trading signals directly to the exchanges. This integration between Alpha Shifter and the exchanges is made posssible by real-time exchange data.
Thanks to the real-time price data, the alerts can automatically be executed at the right price. The order status is sent back from the exchange to Alpha Shifter, ensuring Alpha Shifter knows whether the order was executed correctly and can act accordingly if needed.
SIDEWAYS FILTER
6. FINAL WORDS
As we finish looking at crypto trading data, it’s clear how important and broad this area is. In the fast-moving world of cryptocurrencies, having accurate and fresh data isn’t just nice to have; it’s a must. In our view, anyone financially involved in the cryptocurrency market should be familiar with the relevant crypto data. Are you an algorithmic trader? Success largely depends on your access to historical data for backtesting your strategies and real-time data to run your crypto bot.
With innovations like AI and quantum computing revolutionizing crypto data analysis, staying adaptable is crucial. Because before you realize it, your strategy might be outdated. If you’re invested in crypto trading bots or other cryptocurrency opportunities, then keeping up with the latest developments in the field of crypto data is certainly one of the minimum requirements. Our blog section offers a good starting point for this. Pairing data with informed choices, ongoing education, and caution is key.
