Alchemist Technical Analysis
| ALCH Crypto | USD 0.08 -0.0003 -0.39% |
As of the 13th of March 2026, the last recorded price for Alchemist is 0.08 per share. Primary technical drivers reflect mean deviation of 5.37, and Risk Adjusted Performance of -0.08. Quantitative analysis incorporates volatility metrics and price behavior to assess directional bias. Metrics are compared to industry averages to assess relative positioning.
Alchemist Momentum Analysis
Momentum indicators are widely used technical indicators which help to measure the pace at which the price of specific equity, such as Alchemist, fluctuates. Many momentum indicators also complement each other and can be helpful when the market is rising or falling as compared to AlchemistAlchemist |
What if' Analysis
Running a what-if backtest on Alchemist AI gives investors a practical way to test how changes in horizon, position size, or market timing might have affected the result. Used properly, this review helps investors decide whether Alchemist's historical reward profile was stable enough to support the current thesis.
| 12/13/2025 |
| 03/13/2026 |
If you invested 0.00 in Alchemist on December 13, 2025 and closed the position today, you would earn 0.00 in aggregate gains. The change equals a 0.0% return on investment in Alchemist on balance over a 90 day window. Alchemist is related to or competes with EigenLayer, EUR CoinVertible, Morpho, Allora, and DIA. Peer context can support comparative analysis. Alchemist AI is peer-to-peer digital currency powered by the Blockchain technology.
Alchemist Upside and Downside Indicators Signals
Upside and downside indicators for Alchemist summarize momentum balance and potential range context for the crypto asset. The signals are presented as informational context for recent price movement.
| Information Ratio | -0.11 | |||
| Maximum Drawdown | 63.34 | |||
| Value At Risk | -10.53 | |||
| Potential Upside | 9.09 |
Alchemist Market Risk Indicators Signals
Market risk indicators summarize volatility and return dispersion for Alchemist. This view provides neutral context for risk and variability.| Risk Adjusted Performance | -0.08 | |||
| Jensen Alpha | -0.98 | |||
| Total Risk Alpha | -0.48 | |||
| Treynor Ratio | 4.7 |
The concept of mean reversion suggests that Alchemist's price will eventually return toward its long-run average. High prices may deter value investors, while unusually low prices often attract buyers who anticipate a recovery.
Technical Indicators
| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
| Risk Adjusted Performance | -0.08 | |||
| Market Risk Adjusted Performance | 4.71 | |||
| Mean Deviation | 5.37 | |||
| Coefficient Of Variation | -855.80 | |||
| Standard Deviation | 8.21 | |||
| Variance | 67.37 | |||
| Information Ratio | -0.11 | |||
| Jensen Alpha | -0.98 | |||
| Total Risk Alpha | -0.48 | |||
| Treynor Ratio | 4.7 | |||
| Maximum Drawdown | 63.34 | |||
| Value At Risk | -10.53 | |||
| Potential Upside | 9.09 | |||
| Skewness | -0.01 | |||
| Kurtosis | 5.59 |
Alchemist AI Backtested Returns
Over the selected 3 months, Alchemist demonstrates an exceptional volatility. It shows an Efficiency (Sharpe) Ratio of -0.11, quantifying negative return efficiency across 3 months. Signal processing identified twenty-three dispersion-based indicators. Please analyze metrics such as mean deviation of 5.37, and risk-adjusted performance of -0.08 to assess dispersion and downside exposure. The crypto owns a Beta (Systematic Risk) of -0.21, which conveys relatively modest fluctuations relative to the market. the mildly negative beta suggests Alchemist provides a partial hedge against market-wide declines.
Auto-correlation | 0.72 |
Good predictability
The autocorrelation profile for Alchemist AI registers good predictability between the two measured intervals. When lagged price patterns show consistency, they can serve as a partial input for modeling Alchemist AI's near-term price behavior. A serial correlation of 0.72 indicates that around 72.0% of current Alchemist price fluctuations can be explained by its historical price movements.
| Correlation Coefficient | 0.72 | |
| Spearman Rank Test | 0.58 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Alchemist technical crypto coin analysis uses price and volume transformations to study behavior. The model references moving averages, relative strength, and price correlation signals.
Technical Analysis
The output start index for this execution was twenty-four with a total number of output elements of thirty-seven. The Average True Range was developed by J. Welles Wilder in 1970s. It is one of components of the Welles Wilder Directional Movement indicators. The ATR is a measure of Alchemist AI volatility. High ATR values indicate high volatility, and low values indicate low volatility.
Technical Analysis Methodology & Indicators
Technical analysis of Alchemist evaluates venue price structure, momentum, and volatility clustering in fragmented markets. Volume and liquidity conditions influence signal reliability. Reduced trading volume may increase short-term pricing variability.
For Alchemist AI, this section uses public market feeds and reference sources with Macroaxis normalization rules applied to keep cross-asset comparisons consistent. Intraday timing differences may exist.
This content is curated and reviewed by:
Raphi Shpitalnik - Junior Member of Macroaxis Editorial BoardAlchemist Technical Indicators
A technical review of Alchemist AI can improve timing discipline by comparing momentum, reversal risk, and confirmation signals across several time horizons. The stronger process confirms one signal with others instead of reacting to one pattern in isolation.
| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
| Risk Adjusted Performance | -0.08 | |||
| Market Risk Adjusted Performance | 4.71 | |||
| Mean Deviation | 5.37 | |||
| Coefficient Of Variation | -855.80 | |||
| Standard Deviation | 8.21 | |||
| Variance | 67.37 | |||
| Information Ratio | -0.11 | |||
| Jensen Alpha | -0.98 | |||
| Total Risk Alpha | -0.48 | |||
| Treynor Ratio | 4.7 | |||
| Maximum Drawdown | 63.34 | |||
| Value At Risk | -10.53 | |||
| Potential Upside | 9.09 | |||
| Skewness | -0.01 | |||
| Kurtosis | 5.59 |
March 13, 2026 Daily Trend Indicators
A technical review of Alchemist AI can improve timing discipline by comparing momentum, reversal risk, and confirmation signals across several time horizons. The stronger process confirms one signal with others instead of reacting to one pattern in isolation.
| Accumulation Distribution | 94,691 | ||
| Daily Balance Of Power | -0.20 | ||
| Rate Of Daily Change | 1.00 | ||
| Day Median Price | 0.08 | ||
| Day Typical Price | 0.08 | ||
| Price Action Indicator | 0.00 |
More Resources for Alchemist Crypto Coin Analysis
A structured review of Alchemist AI often starts with core financial statements and trend context. Financial ratios provide context for profitability, efficiency, and growth trends.Alchemist has a market cap of 4.39 M. Use Trending Equities to explore allocation context. This includes a position in Alchemist AI across the allocation. Also, note that the market value of any cryptocurrency could be closely tied with the direction of predictive economic indicators such as signals in inflation. Analysis related to Alchemist should be read together with other portfolio and risk tools before capital is reallocated. That is especially important when the goal is to improve the overall mix of instruments already held. You can also try the Investing Opportunities module to build portfolios using our predefined set of ideas and optimize them against your investing preferences.