I’ve developed a trading strategy that seems to perform exceptionally well based on backtesting results.
I designed this strategy for a trading bot, and it appears to be quite effective. The backtest I ran on TradingView shows impressive results over four years of Solana trading, focusing solely on long positions and not taking any shorts. Most charts I’ve analyzed yield similar outcomes. It starts with a $1,000 capital and invests 100% of the equity.
I can’t shake the feeling that I might be overlooking something, as the level of profit seems unreal. It’s possible that the results are influenced by the strategy starting during Solana’s early days, yet even in its more established phase, the profitability remains strong. I’ve got my bot ready to go and plan to use a Raspberry Pi to run it continuously for a few years, but I can’t help but wonder if the actual profits will be anywhere near these backtested figures. I’d love to hear from anyone who has had similar experiences or insights!
One response
It’s great to see your enthusiasm and success with your trading strategy! However, your hesitations are valid and it’s good that you’re thinking critically about your results. Here are a few points to consider that might explain the discrepancy between your backtest results and real-world trading:
Historical Data Bias: Backtests are only as good as the data used. If your backtest starts at a period when Solana was relatively new and volatile, it might not reflect the market dynamics as they evolved. The later months or years may show different behaviors.
Overfitting: There’s a risk of overfitting your strategy to historical data, making it less effective in live conditions. Ensure your strategy is robust and can perform well under different market conditions.
Market Conditions: The cryptocurrency market can be influenced by numerous external factors (regulatory changes, market sentiment, etc.) that may not be present in historical data. What worked in the past may not necessarily work in the future.
Slippage and Transaction Costs: Backtesting often doesn’t account for slippage (the difference between expected price and actual execution price) or transaction costs, which can eat into your profits significantly, especially with high-frequency trading strategies.
Liquidity Issues: Depending on your trading volume, there might be liquidity issues, especially in smaller market caps or during volatile periods, which can affect the execution of your trades and thus your overall profitability.
Psychological Factors: When trading live, emotions play a big role. It can be challenging to stick to a strategy during periods of high volatility or drawdowns.
Sustainability and Scalability: What works with a small capital might not work the same way when larger sums are deployed due to market impact.
Before deploying your bot in a live market, consider running it in a paper trading or simulated environment to understand how it performs under different scenarios. Additionally, diversifying your approach could help to manage risk. Good luck, and happy trading!