How to achieve 'consistent winning' by trading BTC? - An in-depth exploration of the core of automated trading code
Before discussing how to achieve 'consistent winning' through trading Bitcoin (BTC), we must first establish a core and realistic understanding: there is no absolute 'always winning' or 'guaranteed profit' in financial trading. The market itself is complex and full of uncertainties. Therefore, a more practical and professional goal should be to pursue long-term, stable positive returns, that is, to establish a trading system with a positive expected value.
For ordinary investors, utilizing automated trading code (i.e., quantitative trading or trading bots) is one of the effective ways to achieve this goal. Because it can overcome common human fears, greed, and indecision, strictly executing the established strategy.
This article will delve into the three pillars of achieving stable profits and detail how to utilize automated trading code to build and execute your trading system.
Three pillars of stable profit: strategy, risk control, and mindset
A successful trading system, whether manual or automated, is built on the following three core pillars:
Effective trading strategy: Your 'profit weapon'.
Strict risk management: Your 'life-saving shield'.
Stable trading mindset: Your 'decision core'.
Automated trading code mainly addresses the third point (mindset), but the quality of strategy and risk control entirely depends on your design and settings.
Path 1: Use mature automated trading platforms (suitable for beginners and most users)
For investors without programming skills, the most direct way is to use mature quantitative trading platforms available on the market. These platforms provide verified, pre-set trading bots; you only need to choose strategies and configure parameters based on your judgment.
Common platforms:
Pionex: Famous for its rich variety of trading bots, offering free strategies such as grid trading, Martingale, and spot-futures arbitrage.
3Commas, Cryptohopper: Powerful trading terminals that allow users to connect multiple exchange accounts and provide complex strategy customization and signal following features.
Detailed explanation of core strategy:
1. Grid Trading (Grid Trading Bot)
Core idea: Automatically 'buy low and sell high' within a set price range. The robot will divide the funds into multiple parts, buying in batches when prices fall and selling in batches when prices rise, continuously earning small profits in a fluctuating market.
Applicable market conditions: Fluctuating market/consolidation market. When BTC prices fluctuate back and forth within a relatively fixed range, the benefits of grid trading are most significant.
Risks and countermeasures:
Unilateral rise: The robot will quickly sell all BTC, missing out on subsequent gains, known as 'missing the boat'.
Unilateral decline: The robot will continue to buy, leading to an increased cost of holding, and the floating loss will grow. If it falls below the set lower limit of the price range, the strategy will stop, and you will hold all the BTC bought.
Countermeasures: Reasonably set the price range, choose currency pairs with good volatility but long-term bullish outlook. When the price breaks through the upper limit of the grid, consider manually stopping the robot or starting a new grid at a higher range. Setting stop-loss points is crucial.
2. Martingale Strategy
Core idea: A 'buy more as prices fall' strategy. After a price drop, increase your investment amount to seek to break even and profit from a small rebound.
Applicable market conditions: Long-term bullish slow bull or pullbacks in a volatile market.
Risks and countermeasures:
Extreme unilateral decline: This is the fatal weakness of the Martingale strategy. Continuous, significant declines can quickly deplete your funds, ultimately leading to huge losses or liquidation.
Countermeasures: Strictly control position size and the multiplier for adding positions! Participate with only a small portion of funds, set a maximum number of times to add positions, and choose long-term fundamentally strong assets like BTC. This strategy is extremely risky and not suitable for conservative investors.
3. Spot-Futures Arbitrage
Core idea: Use the funding rate mechanism of perpetual contracts for arbitrage. When the funding rate is positive, market bullish sentiment is strong, and longs must pay fees to shorts. At this time, you can 'buy an equivalent amount of spot BTC' and simultaneously 'short an equivalent amount of BTC perpetual contracts'. This way, regardless of price fluctuations, your spot and contract profits and losses largely hedge each other, but you can steadily earn the funding rate.
Applicable market conditions: Periods where the funding rate is long-term positive and stable (usually in a bull market).
Risks and countermeasures:
Funding rate turns negative: If market sentiment reverses and the funding rate turns negative, you will start paying fees.
Pin risk/liquidation risk: In extreme conditions, the contract price and spot price may deviate significantly, leading to liquidation of the contract position.
Countermeasures: Choose low leverage or no leverage, reserve enough margin, and closely monitor changes in the funding rate.
Path 2: Write your own automated trading code (suitable for advanced users with programming skills)
If you have a basic understanding of programming languages like Python, you can build your own trading robot. This gives you the greatest flexibility and customization.
Basic Process:
Obtain API key: Apply for an API Key at your chosen exchange (such as Binance, Coinbase Pro, OKX, etc.) for programmatic ordering and data querying.
Choose framework/library:
CCXT: A very popular Python library that encapsulates the APIs of hundreds of exchanges worldwide, allowing you to trade on different platforms with a single codebase.
Freqtrade: An open-source cryptocurrency trading bot framework, powerful, supporting strategy development, backtesting, optimization, and live trading.
Backtesting.py: A lightweight Python backtesting framework that can help you quickly validate the effectiveness of strategies on historical data.
Development and backtesting:
Strategy logic: Convert your trading ideas into code. For example, 'Buy when the 20-day moving average crosses above the 60-day moving average, sell when it crosses below' is a simple trend-following strategy.
Historical data backtesting: This is a crucial step. Use historical candlestick data to simulate how your strategy would have performed in past markets.
Evaluation metrics: Focus on the following metrics to assess the quality of the strategy:
Total Return
Sharpe Ratio: Measures return per unit of risk.
Max Drawdown: Measures the maximum loss that the strategy may encounter.
Win Rate
A simple Python backtesting example (using backtesting.py)
Python
from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from backtesting.test import SMA, GOOG # Example using built-in data
class SmaCross(Strategy):
n1 = 10 # Short-term moving average period
n2 = 20 # Long-term moving average period
def init(self):
close = self.data.Close
self.sma1 = self.I(SMA, close, self.n1)
self.sma2 = self.I(SMA, close, self.n2)
def next(self):
# If the short-term moving average crosses above the long-term moving average and there is no current position, then buy
if crossover(self.sma1, self.sma2) and not self.position:
self.buy()
# If the short-term moving average crosses below the long-term moving average and there is a current position, then sell
elif crossover(self.sma2, self.sma1) and self.position:
self.position.close()
# Assume GOOG is the historical price data of BTC
bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002)
stats = bt.run()
print(stats)
bt.plot()
Key risk management and psychological construction
Whether using platform bots or writing your own code, the following principles are key to whether you can survive and profit in the long term:
Always prioritize stop-loss: Any strategy must have a clear exit mechanism. In automated trading, this means setting a stop-loss price or maximum drawdown threshold which will automatically close positions once triggered.
Reasonable position management: Do not invest all funds into one strategy or one trade. Diversify your investments and adjust the proportion of funds based on the strategy's risk.
Avoid over-optimization (Overfitting): When backtesting, do not overly adjust parameters just to make historical data look perfect. A robust strategy that has performed well over the past decade is far superior to one that only performed spectacularly in last year's bull market.
Trust the system and be patient: Even the best systems will experience periods of loss. The advantage of automated trading is that it can continue execution without emotional interference. Do not easily doubt and shut down a thoroughly backtested system due to short-term losses.
Conclusion: There is no holy grail, only systems
Returning to the original question: 'How can one consistently win trading BTC?' The answer is: by establishing and strictly executing a validated trading system with a positive mathematical expectation, supplemented by iron-clad risk management.
For most people: Starting with grid trading bots from platforms like Pionex is an ideal choice. It has a simple principle, relatively controllable risk, and allows you to understand the charm of quantitative trading in practice.
For ambitious developers: Learn to use Freqtrade or CCXT + backtesting.py, embarking on the path of customized strategies.
Remember, automated trading code is not a money printer, but an amplifier. It can amplify the advantages of your strategy, but it can also amplify the flaws in your risk control. Therefore, continuous learning, ongoing optimization, and respect for the market are the only paths to the realm of 'consistent winning'.
