The C91306 trading strategy has an actual-cadence extension with 48 predetermined cells. It looks for daily HIGH20 or hourly HIGH480 events where the prior daily ATR20 excludes the signal day. The strategy executes the first eligible 15-minute open immediately or at the actual manager's UTC schedule, which is daily at 00:02, 04:06, 08, and weekdays at 12:30, 13:50, 16:30, 19:40, and 21. If signals collide, the first event is retained and expires after 2 hours. A delayed price must still exceed the frozen high. The strategy uses a stop of 2ATR and sells a quarter of the position with a gross return of +2, moving the stop to the entry point only at the selected cadence. The residual target is solved ex ante for an aggregate net return of 3, including 25 basis points credited-asset fees and 10/30 basis points slippage. Stops and targets are checked every 15 minutes, and overshoots are charged. A 20-day time exit waits for the selected cadence. Each training period from 2024 to 2025 and the known holdout period from January 1, 2026, to September 12, 2026, starts flat, and the final available close is liquidated with costs. The Vectorbt from_orders ledger cash agrees with the results to less than 1e−5. The strategy has 8 cells with different modes, windows, slippage, returns, drawdowns, win percentages, and numbers of trades. The results show that increasing slippage changes fill-anchored exits and subsequent occupancy. The strategy is missing 15-minute data for BTC and ETH, and SOL data from August 26, 2024, is incomplete. The conclusion is to reject promoting this rule now because all daily manager holdouts are negative, and hourly BTC and SOL turn negative at 30 basis points slippage, while ETH manager is already negative. No fresh entry exists. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars, https://docs.alpaca.markets/us/docs/crypto-fees, and the user's UTC schedule.
A news scan found the text about the C91306 execution unit economics. The PrimaryAlpaca fee schedule, retrieved on September 13 from https://docs.alpaca.markets/us/docs/crypto-fees, lists tier 1 taker fees at 25 basis points and maker fees at 15 basis points for 30-day volume of $0-100,000 USD. The buy fee is charged in the credited coin and the sell fee is credited in USD. This is the documented schedule, not the actual account fee ledger or archived historical fees. Let E be the actual entry fill, r be 2 ATR (Average True Range, a measure of volatility) divided by E, and k be 0.9975 squared times 0.999 for 25 basis point fees plus 10 basis point sell slippage (slippage refers to the difference between expected and actual trade prices). The net stop equity factor is k times 1 minus r. The full net 3R target multiplier T is 1 plus 3 times 1 minus k times 1 minus r, all divided by k. On September 12, the illustrative E was 772, with exact E values of 77,343.651385 for BTC, 2,527.570045 for ETH, and 101.8672655 for SOL. The r values were 6.0381%, 7.4699%, and 10.7150%, with net losses of 6.6008%, 8.0241%, and 11.2497%. The full target gains were 20.5241%, 24.8197%, and 34.5551%. If the quarter is at plus 2r, the residual T is equal to 1 plus 3 times loss divided by k, minus 0.25 times 1 plus 2r, all divided by 0.75, resulting in gains of 23.3401%, 28.1130%, and 38.9300%. These are arithmetic required prices, not fair values or claims that they will trade. The premises are that fees reduce credits on both sides, partial profit reduces capital participating in the tail, and borrowing gross 3R across policies misstates payoff. Liquidity suppliers and the venue collect costs, even on false breakouts. The conclusion is that a defensible measured range and conditional net edge are required before target adoption, and it is better to keep cash rather than shrink the stop to manufacture 3R. The prices and ATR data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
The automated trading fund manager made some premises. These are that quiet weekend prices do not affect 20-day risk, and cross-coin paths must consider common shocks. The manager also decided that unconditional resampling does not validate an entry. The manager used 10,000 synchronized paths, which is called a seed, and 480 hours of data. The data was sampled in 24-hour blocks, starting on Sunday, across Bitcoin, Ethereum, and Solana. The manager trained the data from 2024-2025 and compared it to known data from 2026. The manager also held out 254 complete joint days of data. The data included a 10 basis point buy slip and a 1.001 initial spot, as well as 25 basis point credited asset fees per side and a 10 basis point sell slip. The current ATR20, which is a measure of volatility, was frozen at the September 12 value. The manager checked for exits every hour and every two hours, using a proxy, not the actual manager engine. The full net 3R targets required rises of 20.5241%, 24.8197%, and 34.5551%. The holdout full target probabilities were 5.73%, 9.77%, and 3.07%, with stops at 54.72%, 57.10%, and 45.70%. The means were -0.9304%, -0.6575%, and -1.2271%. The quarter at +2 gross R, stop entry, residual target solved for combined net 3R required gains of 23.3401%, 28.1130%, and 38.9300%. The final target probability was 3.56%, 6.92%, and 1.76%, with means of -0.9019%, -0.6034%, and -1.2217%. The approximate binary mean half Kelly was 0 for all three holdouts. The training positive Bitcoin or Solana plug-ins were not adopted. Three probes of $29.785 were made, totaling $89.355. The managed proxy mean PnL was -$0.8123, P1 was -$9.2638, and the worst sample was -$10.8530, which is a 1.275% account loss. It was noted that no loss greater than 3% in 10,000 paths is not a hard bound, and empirical blocks omit new jumps, structural changes, liquidity failure, and intrahour excursions. The individual managed P99 loss was $2.6158, $3.2233, and $4.2212. A counterargument was made that trend-conditioned fresh entries can outperform unconditional blocks, but these odds neither prove nor veto a valid conditional edge. The actual size was 0 because fresh entry was absent and the same policy manager holdout was weak. All 16 scenario rows were recorded. The data was sourced from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the following analysis of cryptocurrency volatility. The analysis looked at the prices of Bitcoin, Ethereum, and Solana from January 1, 2026, to September 12, 2026. It compared the volatility of these prices on weekends to weekdays. The volatility, or standard deviation, was 33.0404 basis points for Bitcoin, 48.8851 basis points for Ethereum, and 58.7092 basis points for Solana on weekends, compared to 51.2783, 66.7165, and 70.1682 basis points on weekdays. The average price change per hour was -0.4173 basis points for Bitcoin, -0.6670 basis points for Ethereum, and -0.5293 basis points for Solana on weekends. The largest price drop in a single hour was -3.5913% for Bitcoin, -4.0245% for Ethereum, and -5.0440% for Solana. The analysis also looked at how the prices of these cryptocurrencies moved together, or correlated, over 6118 hours. It found that the correlation between Bitcoin and Ethereum was 0.887966, between Bitcoin and Solana was 0.837870, and between Ethereum and Solana was 0.848997. This was higher than the correlation in the training data. The analysis concluded that when evaluating the risk of a portfolio of these cryptocurrencies, it is important to consider their joint exposure, rather than treating them as independent investments. The data for this analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A study examined how trend and weekend conditions affect trading outcomes. The analysis used a sample of Bitcoin, Ethereum, and Solana trades from June to August, with a 10 basis point slip and no delay. It found that groups with trends above the prior 200-day Simple Moving Average, a measure of market direction, had mean returns of 1.6307%, 0.5414%, and 5.6100% for each coin, based on 1, 2, and 1 trades, respectively. In contrast, groups below the Simple Moving Average had mean returns of 0.6147%, 2.7189%, and 0.2757%. The study also looked at weekend trades, which had mean returns of -2.8514% for Bitcoin, and 0% for Ethereum and Solana. Daily trend groups had mean returns of 12.2254%, 10.8368%, and 2.9634%, but these results are not independent because they involve the same coins and market rally. The study used a bootstrap method, a statistical technique, to estimate the mean returns and found 95% confidence intervals of -3.2307% to 5.6745% for Bitcoin, -2.6831% to 7.7079% for Ethereum, and -5.5853% to 8.9792% for Solana. Removing the best trade from each group resulted in significantly lower returns, illustrating the impact of a few exceptional trades. The study concluded that adding a weekend or Simple Moving Average filter, or choosing a delay after the holdout period, is not necessary. The current daily trend remains the context for trading decisions, and new entries should be considered separately. The data for this study came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline 'C91306 SOL hourly: delayed gross3R breakout control'. This refers to a trading strategy for the cryptocurrency SOL, where a 'gross3R' means a target return of three times the risk, and 'breakout control' is a method to manage risk. The strategy uses a'stop', which is a price level at which to sell a security, and a 'target', which is a price level at which to buy a security. The strategy also uses 'ATR20', which stands for Average True Range over 20 days, a measure of price volatility. Each training period starts with no existing positions, and the strategy uses a'reindex' to adjust the calendar and ensure there are no missing data points. The strategy freezes the ATR calculation, excluding the signal, at the start of each day, and only buys if the price exceeds the previous high. The strategy sells at the start of each hour if the price has exceeded the target, and includes a 'terminal close cost', which is the cost of closing a trade. The strategy also includes a 'coin buy fee' of 25 basis points, and a'sell fee' of 25 basis points plus a'slip' of 10 or 30 basis points, which is the difference between the expected and actual price of a trade. The results are summarized in arrays, including the window of time, delay in hours, slip in basis points, return percentage, drawdown percentage, win percentage, number of trades, and a 'half Kelly' measure, which is a formula to determine the optimal size of a trade. The results are as follows: for the training period, the return was 25.81%, the drawdown was 38.76%, and the win percentage was 54.55%, and for the holdout period, the return was 4.70%, the drawdown was 22.48%, and the win percentage was 50%. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'C91306 ETH hourly: delayed gross3R breakout control'. This refers to a trading strategy for Ethereum, a cryptocurrency. The strategy uses a 'gross3R breakout control', which means it aims to make a return of three times the risk taken. The strategy is backtested on historical data from 2024 to 2025, and evaluated on data from 2026. Each test starts with no existing position, and uses a'stop' based on the average true range of the price over the last 20 days. The strategy targets a return of three times the risk taken, and exits positions at the hourly open price if the price overshoots the target. The results are summarized in arrays, which include the window of time, delay hours, slip in basis points, return percentage, drawdown percentage, win percentage, number of trades, and a metric called 'half Kelly'. The arrays show the results for different combinations of these parameters. The strategy is not authorized for live trading, and is compared to a separate 15-minute manager that uses the same policy. The results are also compared to a 'net3R' target, which is not the same as the 'gross3R' target used here. A 'net3R' target would be a return of three times the risk taken, after fees and other costs. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline about a Bitcoin trading strategy. The strategy had predeclared HIGH480 false-to-true events, which means it looked for specific price movements, 2 prior daily Average True Range, or ATR, which is a measure of price volatility, 20 stop, gross 3R target, which means the strategy aimed to make three times the risk, and 20-day exit. Each training period from 2024 to 2025, and a known holdout period from January 1, 2026, to September 12, 2026, started with no existing positions, had a warm-up period in 2023, and used a full calendar reindex with no missing bar signals. The strategy used a freeze on Average True Range excluding signal on UTC day, and a delayed open that must exceed the frozen high. It exited at hourly opens with overshoot, and terminal close was costed, meaning it included all fees. The strategy credited 25 basis points for coin buy fees, and 25 basis points for sell, plus 10 or 30 basis points for slippage, which is the difference between expected and actual trade prices. The results were reconciled using vectorbt from orders final cash, which is a method to calculate the final cash balance, and the difference was less than 1e−5, which is a very small number. The summary arrays showed the results for different scenarios, including window, delay hours, slip basis points, return percentage, drawdown percentage, win percentage, number of trades, and half Kelly, which is a measure of the optimal bet size. The results were: [[
Diagnostic C91306 is a test with 60 predeclared cells. It looks for a false-to-true close above prior 20 calendar day highs on a daily basis or 480 hour highs on an hourly basis. The training period is from 2024 to 2025, and the known period is from January 1, 2026, to September 12, 2026, with a warm-up period in 2023. The system freezes the Average True Range (ATR) over 20 periods, excluding signal times on UTC days, and enters at the next open or delays entry. If the delayed open is less than or equal to the frozen high, the entry is cancelled. The stop entry is set at -2 ATR, the target entry at +6 ATR, and the time limit is 20 days. The system checks at each sampled open and charges for gap overshoots and terminal close costs. The gross 3R target is not the same as the net 3R or the mandated quarterly/BE policy, and this is not a live authorization. The system credits 25 basis points for coin buy fees, 25 basis points for sell fees, and 10/30 basis points for slippage, with a custom order ledger that reconciles to less than 1e-5. There is no fill-forward for missing prices in signals, and the calendar is reindexed. The SOL training is incomplete. The results are as follows: there are seven sets of results with different parameters, including return, drawdown, win rate, sample size, mean trade percentage, half Kelly fraction, skipped entries, days, symbol, time frame, delay, slippage in basis points, and window. The models ignore historical quotes, depth, and vintages, and idealize bar-open execution. The data comes from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A diagnostic report C91306 was generated with 60 predeclared cells. The report indicates a fresh false-to-true close above prior 20 calendar day highs on a daily basis or 480 hour highs on an hourly basis. The training period is from 2024 to 2025, and the known holdout period is from January 1, 2026, to September 12, 2026. Each period starts with a flat position. The report also mentions a warm-up period in 2023. The trading strategy involves freezing the Average True Range (ATR) 20, excluding signal UTC day, and entering at the next open or delaying entry. If the delayed open is less than or equal to the frozen high, the entry is cancelled. The stop entry is set at -2 ATR, and the target entry is set at +6 ATR, with a time limit of 20 days. The report checks for these conditions at each sampled open and charges for gap overshoots and terminal close costs. The gross 3R target is not the same as the net 3R or mandated quarter/BE policy, and this is not a live authorization. The report also mentions a 25 basis point buy fee and a 25 basis point sell fee, with a 10/30 basis point slippage. The custom order ledger reconciles with a final cash value of less than 1e-5. There is no fill-forward missing prices for signals, and the calendar is reindexed. The SOL training is incomplete. The report includes a list of results for different scenarios, including returns, drawdowns, win rates, and mean trade percentages. The results are for the symbol ETH, with a time frame of 1 day, and different delays and slippage values. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A diagnostic report, labeled C91306, analyzed 60 cells of data. The report looked for instances where the closing price was higher than the highest price in the previous 20 calendar days or 480 hours. The training period was from 2024 to 2025, and the known period was from January 1, 2026, to September 12, 2026. Each period started with no prior knowledge, and there was a warm-up period in 2023. The report used a method called ATR20, which stands for Average True Range over 20 periods, excluding signal times on UTC days. Entries were made at the next open or delayed, and delayed opens were canceled if they were less than or equal to the frozen high. The stop entry was set at 2 ATRs below the entry point, and the target entry was set at 6 ATRs above the entry point, with a time limit of 20 days. The report checked for these conditions at each sampled open and accounted for gap overshoots and terminal close costs. The target was a gross 3R, which means a three times return, not a net 3R, and it did not follow a mandated quarter or best efforts policy. This was not a live authorization. The report included a 25 basis point buy fee and a 25 basis point sell fee, with a 10 to 30 basis point slippage. The results were as follows: a list of trading results for Bitcoin, including return, drawdown, win rate, sample size, mean trade percentage, half Kelly fraction, skipped entries, days, symbol, time frame, delay, slippage in basis points, and window. The results showed varying returns and drawdowns for different scenarios. The data source was https://runway.adityaperswal.workers.dev/data/crypto-bars, and the documentation for crypto fees was https://docs.alpaca.markets/us/docs/crypto-fees. The models ignored historical quotes, depth, and vintages, and idealized bar-open execution. A separate experiment compared the 15-minute manager.
A review of the query_trades fund for crypto at 06:02 UTC found one trade. This trade, number 10, involved buying and selling Bitcoin. The details of the trade were: entry price 81614.8, exit price 81548.7, quantity 0.001681857. The trade was opened on September 3, 2026, at 19:42:24 UTC and closed at 20:30:20 UTC by an operator who decided to liquidate, or sell, the asset. The price return was a loss of 0.0809902 percent, resulting in a loss of 0.111171 USD. The trade was not successful, with zero wins out of one trade. The stop loss price of 73440 and target price of 97900 were not triggered. There is no information about the reference quote at the time of entry or itemized fees, so it is not possible to determine slippage, which refers to the difference between expected and actual prices. The premises, or assumptions, of this trade do not support the idea that the payoff, or return, was based on a valid strategy. The old reason for the breakout, or sudden price movement, does not justify the delayed entry into the trade. Therefore, it is not possible to conclude that this trade was based on empirical, or observed, evidence of a successful strategy, specifically the Kelly criterion, which is a formula used to determine the optimal amount to bet. Currently, there are no open or pending positions. The source of this information is the query_trades fund for crypto and the venue account context provided on September 13.
The account starts with $850.99 in equity and $119.14 in cash and crypto allocation, with no current positions or orders. The goal is to have a positive expectancy with an asymmetric payoff. A fresh entry under a validated policy is required, not just available cash. The deep dive tested if stablecoin growth provides useful information beyond price momentum. It found that different factors like issuance and payments are distinct, and historical definitions are important. Eighteen tests showed that supply confidence intervals cross zero, but this does not prove causality. The prior is only weakly updated toward using supply as context. A filter using USDC growth and SMA200 produced returns of -2.1408% for BTC, +3.6668% for ETH, and +5.4333% for SOL, which are below the controls. This filter is rejected. Current market conditions still fail the HIGH480 test for top-ten names. A new strategy cannot justify buying an old entry. The conclusion is to retain $119.14 in cash and accept missed-upside risk rather than weaken the entry policy. This decision is not a bearish market forecast. A fresh tested entry with a defensible net payoff can change the decision. Maintenance tasks were performed, including replacing and verifying data. Sources include Circle and Llama websites.
A review of the C91304 live trade record found that it cannot estimate the stablecoin rule's edge. The query_trades fund for crypto returned one closed Bitcoin lot with an entry price of 81614.80, an exit price of 81548.70, and a quantity of 0.001681857. The trade was opened on September 3 at 19:42:24.677 UTC and closed at 20:30:20.345 UTC due to operator liquidation. The raw price return was -0.0809902%, with a price-PnL of -0.111171 USD and 0 out of 1 wins observed. Neither a stop nor a target determined the exit, which is a type of order that automatically sells or buys a security when it reaches a certain price, known as a stop-loss or take-profit target. There was no entry quote or reference price, and no separate fee ledger to support a slippage estimate, which refers to the difference between the expected price of a trade and the actual price at which the trade is executed. The old price-breakout thesis, a trading strategy based on the idea that a stock's price will continue to move in the same direction after it breaks out of a established range, was not used tonight, instead the supply rule was applied. The premises of this review are that one externally terminated trade is not enough to determine a strategy distribution, and that win/loss odds or half-Kelly, a formula used to determine the optimal bet size, should not be estimated from this single trade. The current own book has no open or pending crypto orders, and EEM, which belongs to the trend category, is outside this review. The source of this information is the query_trades fund for crypto, and the user venue context is as of September 13 at 04:01 UTC. The conclusion is to use recorded research controls, rather than relying on 0/1 pseudo-confidence, with an actual allocation of 0.
A test was run using a computer program called a node with a script called backtest.js. The test looked at three cryptocurrency pairs: BTC/USD, ETH/USD, and SOL/USD, using a strategy called sma-cross, which is a type of trading strategy that uses moving averages. The test used a fast moving average of 1 day and a slow moving average of 200 days, and it looked at 981 days of data. The results were: for BTC/USD, a gross return of 14.4761, a maximum drawdown of 31.2332, a win rate of 15.3846, and 13 closed trades; for ETH/USD, a gross return of 10.7680, a maximum drawdown of 37.8551, a win rate of 11.1111, and 9 closed trades; and for SOL/USD, a gross return of 8.3228, a maximum drawdown of 32.9545, a win rate of 0, and 3 closed trades. The test used the same closing price for execution and did not include fees or holdouts. The data used for the test included 982 bars for BTC and ETH, and 749 bars for SOL, including a partial bar from September 13, and the open and mark-to-market prices explain the positive total return for SOL despite having no closed winning trades. The test cannot implement a stable coin signal and only uses mandatory command line interface control. The costed daily and hourly data, as well as the holdouts, were recorded separately. There were no orders or choices made from the gross output. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A companion to 3130, this analysis uses the same lag 2 seven-day USDC unit growth and prior 7-day price momentum, with next 1-day log return as the outcome, using a method called OLSHAC 7, which is a statistical technique. The analysis excludes the final training day from 2024 January 1 to 2025 December 30, and uses known holdout outcomes from January 1 to September 11 through September 12. The analysis uses data from BTC/ETH with 730 training data points and SOL with 485 training data points, and 254 holdout data points. The training results show betas of -0.001839, +0.016259, and -0.104505, with p-values of 0.968210, 0.780891, and 0.114559. The holdout results show betas of 0.088794, 0.122204, and 0.136675, with p-values of 0.365205, 0.456096, and 0.302054, and 95% confidence intervals of [-0.103403, 0.280991], [-0.199167, 0.443575], and [-0.122887, 0.396236]. All results cross zero, meaning no one-day leading effect was established. This analysis fulfills the originally specified 1/7 day resolution, and is not a new chosen winning horizon. The results are limited by revision and availability uncertainty, omitted common macro drivers, and small regimes, and do not imply causality or order. The sources of the data are https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars.
The current trend of C91304 is positive. On September 12, the closing prices of Bitcoin, Ethereum, and Solana were 77266.385, 2525.045, and 101.7655, respectively, which exceeded the Simple Moving Average, or SMA, of 200 days, which were 70095.0660, 2055.8333, and 83.04133. The Average True Range, or ATR, for 20 and 200 days were 335.03218 and 94.40365 for Bitcoin, 5.45756 and 4.53365 for Ethereum, and 5.45756 and 4.53365 for Solana. However, on September 13 at 03 UTC, the high prices failed to exceed the previous day's high, with Bitcoin at 77184.35, which was less than 82233.50254, Ethereum at 2520.3465, which was less than 2664.8535, and Solana at 101.575, which was less than 110.5357. The same collector for all seven days showed false, and the last pullback for Ethereum was available on September 11 at 22 UTC, with no new arm. A positive 200-day trend does not create a fresh high or pullback retroactively. For illustration purposes only, an entry on September 12 at the close price multiplied by 1.001, with a stop at 2 times the ATR, and a full exit with a net of 3 times the risk, or 3R, with a 25 basis point entry coin and exit USD fee, plus a 10 basis point sell slip, would require gains of 20.5241, 24.8197, and 34.5551 percent for Bitcoin, Ethereum, and Solana, respectively. The targets would be 93217.7762, 3154.9047, and 137.06755, with an unconditional 2026 rolling 20-day Maximum Favorable Excursion, or MFE, of 9018.1013, 23.8079, and 23.1779 percent, and coverage of 8.5106, 8.5106, and 7.2340 percent. These full-exit targets are not the mandated plus 2R quarter or trailing policy, and the final target bridge must be solved separately. For illustration purposes only, the quarter net stop loss would be 29.785, with a net stop loss of 1.9660, 2.3900, and 3.3507 USD for Bitcoin, Ethereum, and Solana, respectively, but no actual levels were adopted. Low unconditional coverage is not an automatic veto on fat-tail trades, but it does not validate an entry. The binding constraint today is a fresh policy entry, and the new stable coin filter failed the incremental holdout, with an actual size of 0. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the crypto fees are documented at https://docs.alpaca.markets/us/docs/crypto-fees.
A computer program called NumPy with the seed 91304 created 10,000 examples of 20-day price paths for Bitcoin, Ethereum, and Solana from 235 overlapping time periods in 2026. It assumed equal initial investments and no changes to the investments. The results showed that the worst 1% of examples had a loss of 40.2133%, which would be a loss of $47.9102 for a full investment of $119.14 or $11.9775 for a quarter investment of $29.785. The daily price changes of these three cryptocurrencies were highly correlated, with correlation coefficients of 0.911160 for Bitcoin and Ethereum, 0.878579 for Bitcoin and Solana, and 0.879436 for Ethereum and Solana. The program assumed that risk increases over time and that these three cryptocurrencies are highly correlated. However, it noted that unexpected events like exchange failures could be worse. These scenarios are based on historical data and do not predict future outcomes. They also do not take into account factors like fees, stops, or rebalancing. A 25% joint crash would cost $29.785 for a full investment, which is 3.5% of the account, or $7.44625 for a quarter investment, which is 0.875% of the account. The program requires current executable entry and policy-specific stops before using size and should not be used to predict future quiet periods. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the text describing a trading strategy for the cryptocurrency SOL. The strategy uses a filter based on the growth of the USDC stablecoin supply and the price of SOL. The filter is applied to the SOL price and is used to decide when to buy or sell. The strategy is tested on historical data from 2024 to 2025 and is evaluated on its performance during the period from January 1, 2026, to September 12, 2026. The results show that the strategy had a return of -11.006206% during the training period and 5.433297% during the evaluation period. The strategy also had a maximum drawdown of 34.649737% during the training period and 10.758956% during the evaluation period. The text also mentions that the strategy is not suitable for actual trading due to the lack of a stop-loss or target profit policy. The data used for the strategy is sourced from several websites, including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'C91304 SOL 1Day: stablecoin filter versus price-only control'. This refers to a test of a trading strategy for the cryptocurrency SOL. The strategy uses a 'frozen baseline' which means it only trades when two conditions are met: the 30-day supply growth of the USDC stablecoin is greater than 0, and the previous day's closing price is higher than the 200-day moving average, a calculation of the average price over the last 200 days. When these conditions are true, the strategy enters a trade at the next available opening price and exits when they are no longer true. The test was run from 2024 to 2025, with a holdout period from January 1 to September 12, 2026, and a warm-up period in 2023. The results showed a return of -11.006206% during the training period, with a maximum drawdown of 33.249904%, and no winning trades. In the holdout period, the return was 5.433297%, with a maximum drawdown of 9.663307%, and 100% winning trades. A control strategy that only looks at price movements had a return of 18.366768% in the holdout period. The test used a 25 basis point fee and a 10 basis point slippage per trade, and assumed a 30 basis point slippage for stress testing. The results are based on data from several sources, including crypto-bars and stablecoin data from llama.fi, and crypto fees from Alpaca markets.
A news scan found the text: Frozen baseline: lag2 calendar-day USDC 30-day supply growth greater than 0 AND previous completed daily close greater than SMA200, which is a moving average of the last 200 days. Enter first available bar open when state becomes true; exit first available open when false. Split starts flat and initializes if state true, terminal liquidation at last close. Training from 2024 to 2025, known holdout from January 1, 2026, to September 12, 2023, warmup, no partial September 13 bar. Vectorbt 100 USD all-in single-symbol independent portfolios, 25 basis points fee plus 10 basis points slip per side; stress 30 basis points slip. Sweeps lag 1, 2, and 7 and combined USDC plus USDT, control price-only; no parameter selection from holdout. Baseline training return 75.031958 percent, drawdown 35.057611 percent, win 33.333 percent, number of trades 9; holdout return 3.666842 percent, drawdown 6.462538 percent, win 100.000 percent, number of trades 2. Price-only holdout 11.301377 percent, number of trades 1. Daily signal mapped to hourly opens; identical fills when 00 UTC exists, hourly marks expose greater intraday drawdown. This is not independent replication or an hourly supply series. Current-vintage supply lacks publication archive; assumed lags cannot remove revisions. Fees modeled in quote currency are a close proxy, not exact venue credited-coin accounting. No stops or targets or plus 2R partial or trailing policy here, so baseline cannot authorize trading; incremental filter is not adopted. SOL has 418 missing calendar days across 2023 to 2024; missing SMA means unavailable, not bearish; trade counts small. Half-Kelly recorded only where both wins and losses exist, uncapped fractions unstable; allocation zero. All 28 cells recorded separately in observations. Sources https://runway.adityaperswal.workers.dev/data/crypto-bars; https://stablecoins.llama.fi/stablecoin/2; https://stablecoins.llama.fi/stablecoin/1; https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing a trading strategy for Ethereum. The strategy has a baseline that freezes when two conditions are met: the 30-day supply growth of the USDC stablecoin, which is a type of cryptocurrency pegged to the US dollar, is greater than 0, and the previous day's closing price is greater than the 200-day simple moving average, which is a calculation of the average price over 200 days. When these conditions are true, the strategy enters a trade at the next available opening price and exits when the conditions are no longer true. The strategy was trained on data from 2024 to 2025 and tested on data from January 1 to September 12, 2026. The results showed a return of 75.031958% during training, with a maximum drawdown of 33.073196% and a win rate of 33.333%. During the test period, the return was 3.666842% with a maximum drawdown of 4.774546% and a win rate of 100%. A control strategy that only considers price movements had a return of 11.301377% during the test period. The strategy uses a 25 basis point fee, which is a type of transaction cost, and a 10 basis point slippage, which is the difference between the expected and actual price of a trade. The results are based on data from several sources, including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing a trading strategy for Bitcoin. The strategy uses a filter based on the growth of a stablecoin, USDC, and its 30-day supply. The filter is combined with a price-only control. The strategy enters a trade when the filter is true and exits when it is false. The training period is from 2024 to 2025, and the holdout period is from January 1, 2026, to September 12, 2026. The warm-up period is in 2023. The strategy uses a 25 basis point fee and a 10 basis point slippage per side. The results show a return of 30.157692% during training, with a drawdown of 36.117294% and a win rate of 27.778%. In the holdout period, the return is -2.140782%, with a drawdown of 6.395037% and a win rate of 50.000%. The price-only control has a return of 10.679989% in the holdout period. The strategy uses a daily signal mapped to hourly opens. The fees are modeled in the quote currency, which is a close approximation but not exact. The strategy does not use stops, targets, or a trailing policy. The data sources are listed as https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text describing a trading strategy for Bitcoin. The strategy uses a filter based on stablecoin supply growth and price trends. It buys when the 30-day supply growth of USDC is increasing and the previous day's close is above the 200-day moving average. The strategy sells when these conditions are no longer met. The training period was from 2024 to 2025, and the test period was from January 1, 2026, to September 12, 2026. The strategy was tested with a 25 basis point fee and a 10 basis point slippage per trade. The results showed a 30.16% return during training, with a maximum drawdown of 33.91% and a win rate of 27.78%. In the test period, the return was -2.14%, with a maximum drawdown of 5.82% and a win rate of 50%. A price-only control strategy had a 10.68% return during the test period. The strategy uses data from sources including https://runway.adityaperswal.workers.dev/data/crypto-bars, https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://docs.alpaca.markets/us/docs/crypto-fees.
A question was asked about whether the growth of USDC units over seven days can predict the next seven-day return of certain cryptocurrencies after considering the previous seven-day price momentum. To answer this, a statistical method called ordinary least squares, or OLS, was used with a special way to account for variations called Newey-West/HAC7 covariance. The data used for training was from January 1, 2024, to December 24, 2025, excluding the last seven days, and for testing from January 1, 2026, to September 5, 2026, with outcomes up to September 12, 2026. The data came from a source called Current-vintage Llama aggregate history, but the publication dates are unknown, and no cause-and-effect relationship was identified. The analysis looked at three cryptocurrencies: BTC, ETH, and SOL. The training data had 724 observations for BTC and ETH, but only 479 for SOL due to missing history, and each had 248 observations in the testing data. The results showed that the relationship between USDC growth and the next return of these cryptocurrencies was not stable. In the training data, the coefficients, or betas, were -0.000083 for BTC, -0.229591 for ETH, and -0.504461 for SOL, and all their 95% confidence intervals included zero. In the testing data, the betas were 0.938418 for BTC, 1.453205 for ETH, and 1.425510 for SOL, with confidence intervals that also crossed zero. Similar results were found when combining USDC and USDT data. The correlations between lagged future values were around 0.25 to 0.28 in 2026, which looks promising, but near zero or negative in the training data. The correlations between contemporaneous trailing prices were generally larger. However, there was a counterexample where the future correlation of SOL with USDC was 0.27246, exceeding the past correlation of 0.24848, showing that supply does not always follow price. The models did not establish a stable predictive effect, and they do not prove there is no effect or direction of causality. The prior assumptions were only weakly updated, and to improve, prospective publication-stamped samples and repeatable net holdout improvement are needed. The sources of the data are https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://runway.adityaperswal.workers.dev/data/crypto-bars.