——— Grid Strategies ———

Let's make a few assumptions:

Asset price series are homoscedastic. In other words, market remains flat and asset price series will not diverge significantly in the near term
We know the average asset price (AAP)
Asset price series are stationary

If the above conditions are true, we can start trading within the price channel using a Classic Grid Strategy — longing at prices slightly lower than AAP and shorting at prices slightly higher than AAP

Unfortunately, assumption 3) breaks because asset price series are never stationary — even during a flat market, they follow either a slow upward or downward trend. Therefore, it is better to use a Long Grid or Short Grid Strategy — buying the dips and selling the peaks, adjusting price levels to follow the trend

In reality, however, assumption 2) also breaks — AAP is notoriously difficult to estimate. This is why nowadays nobody uses vanilla Grid Strategies in their pure form. A slightly more modern approach is to integrate indicators and signal-based entries (while truly cutting-edge models go even further, as discussed later)

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Summary: It is difficult to design a strategy that performs well during both flat and volatile markets, because assumption 1) also breaks. Typically, a system optimized for a range-bound environment will fail during a breakout, and vice versa. In fact, once the market regime is identified, selecting a corresponding strategy becomes relatively straightforward. Therefore, I believe that forecasting market volatility is one of the most critical (yet challenging) tasks in algorithmic trading

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——— Delta Neutral Strategies ———

Delta-neutral strategies are designed to keep the total portfolio value constant during asset price fluctuations. I am familiar with the following currently popular approaches:

🔥 Liquidity Pools (LP) with a corresponding Short position: While often marketed as delta-neutral, this approach is technically "pseudo-delta-neutral" due to the non-linear nature of LP positions — static Short hedge only provides neutrality at a specific price point. Continuous rebalancing is required to offset Impermanent Loss, which often makes the net yield unattractive after transaction costs. As I said earlier — "strategies which work during flat periods fail during volatile periods"
🔥 Cash-and-Carry Arbitrage: In the crypto market, Perpetual Futures use a Funding Rate. ****When bullish sentiment prevails (Long dominance), the funding rate is positive: Long holders pay Short holders. Since the crypto market is bullish most of the time, the rate remains positive. Quants exploit this by opening a Short position backed by an equivalent Spot purchase. The Delta-Neutral state is achieved by executing two simultaneous actions: Purchasing some BTC (or other coin) on the spot market and shorting the exact same amount on the perpetual futures market. As a result, your USD balance remains stable while you continuously collect the Funding Rate payments
🔥 On-Chain Atomic Arbitrage (DEX-to-DEX): I consider Atomic Cross-DEX Arbitrage to be a pure Delta-Neutral strategy. This approach exploits price discrepancies between different Decentralized Exchanges (e.g., Uniswap and Sushiswap) within a single blockchain transaction

• Execution: A custom Smart Contract executes a series of swaps (e.g., Buy ETH on DEX A, Sell ETH on DEX B) simultaneously • Risk Mitigation: The strategy utilizes the atomic nature of blockchain transactions. The contract includes a "Check-and-Revert" logic: if the final balance is not greater than the initial balance (accounting for gas fees and slippage), the entire transaction fails and reverts • Market Neutrality: Since the buy and sell occur within the same block, the exposure time is near-zero. This makes the strategy completely immune to market direction, as there is no "held" position at any point in time

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Summary: In my view, while the strategies above are essential tools, they are largely commoditized. I believe the real alpha lies not in simple hedging, but in predictive modeling of volatility regimes mentioned earlier. By identifying regime shifts before they materialize, we can dynamically adjust our exposure and leverage, turning a simple delta-neutral position into a high-performance systematic engine

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——— Strategy Performance ———

To systematically evaluate the algorithm's health, I categorize KPIs into three distinct groups:

✅ 1) Risk & Stability Metrics:

🔥 Total Number of Trades: Sample size is vital for statistical analysis. In econometrics, the Law of Large Numbers only begins to apply after the number of observations exceeds 100, which is the minimum threshold for a sample to become statistically significant. If the Total Number of Trades is low, all other metrics must be considered invalid. Consequently, if two strategies have identical performance metrics, but one has 100 trades while the other has 1'000, the latter is objectively superior due to its higher statistical reliability and lower probability of being a result of a pure "random walk"