The Protocol 2230 with corrections 2233 and 2236 was used from January 2, 2019, to December 30, 2022, with calibration from 2016 to 2018 and a training period from 2019 to 2022, and a holdout period from 2023 to August 26. The strategy involved rebalancing on the first monthly open with constant calibration exposure and terminal liquidation. The position size was N1, with monthly win frequency calculated separately. Fees were 1 basis point per side, with a 5 basis point slip, and cash had a 0 yield. The return was 24.453406075542116 percent, with a drawdown of 30.354470021404133 percent, and a win rate of 100 percent over 48 months. The vectorbt daily reconciliation had a maximum error of 0 at 10,000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted, with a proxy for survivorship, and the history was not globally untouched. This was a diagnostic instrument, with no winner promotion. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results can be found in research/test_vm.py/results.json.
A news scan found the report of an automated trading fund manager. The report discusses the performance of an Ethereum trading strategy. The strategy uses a 480-hour high and 240-hour low, with a 14-day average true range, or ATR, which is a measure of price volatility. The strategy aims to make a 3R return, which means making three times the risk amount, with a 20-day timeout and a 2R cost basis, or breakeven, and a peak 2ATR trailing stop. The strategy enters trades at the next hourly open after a signal, and exits are sampled at hourly opens, with missing opens skipped and no guaranteed stop fills. The strategy credits a 25 basis point fee per side, which is a 0.25% fee, and 10 basis points of lippage, which is a measure of the difference between the expected and actual price of a trade, as a baseline, with a 20 basis point stress test that freezes the baseline target. The strategy uses a cash-only vector and initializes with a cash amount of 1.0, with raw wealth and closed trade fee denominators reconciled to less than 1e-10 across 48 cells, with a maximum of 2.45e-15. The report compares the performance of the strategy in two time periods: from 2024 to 2025, and from January 1, 2026, to September 11, 2026. The training return was 33.25%, with a maximum drawdown of 39.72%, and 19 trades. The holdout return was 17.24%, with a maximum drawdown of 19.83%, and 6 trades, with a win rate of 66.67% and a mean return of 2.87%. The stress test return was 15.84%, with a static return of 17.24%, and a buy-and-hold return of -17.60%. The report notes that all current frozen entries are false, and that daily and hourly policies are distinct, with no equal return requirement. The report reproduces a prior return of 17.24% with six trades exactly, but notes that repeated calculation is not new independent proof. The report concludes that it is for research watch only, and no order should be placed. The report notes that the accounting is valid within assumptions, but that unknown historical bid-ask spreads, 2025 bar anomalies, small sample size, and off-run exact trail implementation limit the soundness of the results. The report cites two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The report is based on observations from 24627 to 24674, with a specification of 2231.
A news scan found the report of an automated trading fund manager for C91106 ETH. The report discusses the daily frozen net 3R replication and cost stress. The premise uses a native daily high and low with a 14-day average true range, or ATR, which is a measure of volatility. The cost-aware target has a 20-day timeout and a 2R cost breakeven, or BE, with a peak 2ATR trailing stop. The target is retained and the signal is given after the bar is completed, with the next exact open. Exits are sampled at daily opens, and missing opens are skipped, with no guaranteed stop fills. The credited asset has a 25 basis point fee per side, which is a measure of the cost, and a 10 basis point lippage baseline, which is a measure of the difference between the expected and actual price. A 20 basis point stress freezes the baseline target. The cash-only vector backtest, or bt, has an initial cash of 1.0, and the raw wealth and closed trade fee denominators are reconciled to be less than or equal to 1e-10 across 48 cells, with a maximum of 2.45e-15. The training period is from 2024 to 2025, with a holdout period from 2026 January 1 to September 11 00 UTC. The training return is 2.0291530569%, with a drawdown, or DD, of 34.9426662999%, and 15 trades. The holdout return is -17.6324659487%, with a DD of 27.3536412981%, a win rate of 20%, and 5 trades, with a mean return of -3.5218771014%. The stress return is -18.452038509%, the static return is -17.6324659487%, and the buy and hold, or BH, return is -16.7705527594%. All current frozen entries are false, and daily and hourly policies are distinct, with no equal return requirement. This report is for research watch only, and no order should be placed. The accounting is valid within assumptions, but there are limitations due to unknown historical bid ask, 2025 bar anomalies, small sample size, and off-run exact trail implementation. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The observations are from 24627 to 24674, and the specification is 2231.
A news scan found the report: Premise 1 uses a frozen rolling 480 hour high and 240 hour low, with a 14-day Average True Range (ATR), which measures price volatility, reconstructed from full 24 hour UTC days. It has a 2 ATR risk, cost-aware 3R target, 20 day timeout, +2R cost breakeven (BE) and peak-2 ATR trailing, target retained. Signals occur after a completed bar, at the next exact open, and exits are sampled at hourly opens, with missing opens skipped and no guaranteed stop fills. The report credits a 25 basis point fee per side and 10 basis point lippage baseline, with a 20 basis point stress that freezes the baseline target. The vectorbt init cash is 1.0, with raw wealth and closed trade fee denominators reconciled to less than or equal to 1e-10 across 48 cells, with a maximum of 2.45e-15. Premise 2 compares the chronological 2024-25 train, with SOL available from August 26, 2024, to the reused 2026 January 1 to September 11 00 UTC holdout, with a flat reset. The training return is 20.5581742344%, with a drawdown (DD) of 28.4815768457% and 19 trades. The holdout return is 7.6357530493%, with a DD of 12.8707598205%, a win rate of 50%, 6 trades, and a mean of 1.4019896435%. The stress is 6.3518399027%, the static is 7.6357530493%, and the buy and hold (BH) is -12.2163852845%. Premise 3 states that all current frozen entries are false, with daily and hourly distinct policies and no equal return requirement. The conclusion is for research watch only, with no order. The report notes valid accounting within assumptions, but unknown historical bid ask, 2025 bar anomalies, small N, and off-run exact trail implementation limit soundness. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, with observations from 24627 to 24674 and specification 2231.
This is a test result for the VM9110600 EFA monthly strategy. The protocol used is Protocol2230 with corrections 2233 and 2236, and the test period is from 2023-01-03 to 2026-08-31, with calibration from 2016-2018 and training from 2019-2022, and a holdout period from 2023 to August 26. The strategy uses a first monthly open rebalance with a capped prior variance of 1 and terminal liquidation. The position size is N1, and the monthly win frequency is calculated separately. The fees are 1 basis point per side, with a 15 basis point slip, and the cash yield is 0. The return is 57.96449093831391% with a drawdown of 11.583296443854197%, and a win rate of 100% over 44 months. The strategy uses vectorbt for daily reconciliation with a maximum error of 0 at 10,000 initial units. There is no 15-minute or whole-share validation, and the data is dividend-adjusted with survivorship bias, but the history is not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results are from research/test_vm.py/results.json.
A news scan found the headline 'C91106 BTC daily frozen net3R replication and cost stress'. The report discusses a trading strategy for Bitcoin (BTC) with specific rules: it freezes the native daily high and low, uses a 14-day average true range (a measure of price volatility) to set a 2 times risk target, and has a 20-day timeout. The strategy also includes a cost-aware target return and a peak 2 times average true range trailing stop. The report notes that signals are generated after a completed bar, and exits are sampled at daily opens, with missing opens skipped and no guaranteed stop fills. It also mentions that a 25 basis point fee per side and 10 basis point lippage (the difference between the expected price and the actual price) are credited to the asset, with a 20 basis point stress freeze on the baseline target. The report references two premises: the first discusses training data from 2024 to 2025, with a return of 1.4238283647% and a drawdown (the maximum loss from a peak to a trough) of 32.3728386495%, and the second discusses holdout data from 2026, with a return of -3.355266979% and a drawdown of 21.0292788335%. The report concludes that all current frozen entries are false, and that daily and hourly policies are distinct, with no equal return requirement. It also notes that this research is for watching only, and no orders should be placed based on it. The report cites two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and references observations 24627-24674 and specification 2231.
This is a report on the VM9110600 EFA monthly train slip15 scale1. The protocol used was Protocol2230 with corrections 2233 and 2236, and the data range was from 2019-01-02 to 2022-12-30, with calibration from 2016-18 and training from 2019-22, and a holdout period from 2023 to August 26. The first monthly open rebalance was done with a prior variance capped at 1, and terminal liquidation was used. The position was labeled as N1, and the monthly win frequency was calculated separately. The fees were 1 basis point per side, with a slip of 15 basis points, and the cash yield was 0. The return was 8.063182173909889 percent, with a drawdown of 22.094108846007956 percent, and a win rate of 100 percent over 48 months. The vectorbt daily reconciliation had a maximum error of 1.8189894035458565e-12 at 10000 initial units. There was no 15-minute or whole-share validation, and the data used was dividend-adjusted and subject to survivorship bias, but the history was not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results can be found in research/test_vm.py/results.json.
A query of trades for the supply fund returned two closed lots, both of which were liquidated by the operator on September 4, rather than being stopped or targeted by the strategy. The entry and exit prices for these trades were 44.49 and 46.18 for FRO, and 79.76 and 80.36 for STNG. These two gains do not provide enough information to estimate the size of potential losses or the likelihood of stop hits or future wins. Additionally, there are no benchmark quotes available to measure slippage from the entry and exit prices. The sources of this information are the fund's trade ledger query and trades 6 and 3. The original reasons for these trades are still claimed, but have not been freshly verified as physical evidence. The conclusion is that the size of trades should be determined from audited, independent history with explicit uncertainty, rather than from these two forced exits or funding pressure.
This is a test result for the VM9110600 EFA monthly strategy. The test used a protocol with corrections, and the data range was from 2023-01-03 to 2026-08-31, with calibration from 2016-2018 and training from 2019-2022, and a holdout period from 2023 to August 26. The strategy had a first monthly open rebalance with a capped prior variance of 1 and terminal liquidation. The position size was N1, with a monthly win frequency calculated separately. The fees were 1 basis point per side, with a 5 basis point slip, and a cash yield of 0. The return was 59.000753911733874 percent, with a drawdown of 11.583296443854207 percent, a win rate of 100 percent, and 44 months of data. The test used vectorbt daily reconciliation with a maximum error of 0, at 10,000 initial units. There was no 15-minute or whole-share validation, and the data was dividend-adjusted, with a survivorship bias, and the history was not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was based on the test_vm.py/results.json file.
This is a report on the performance of the VM9110600 EFA monthly train slip5 scale1. The report covers the period from 2019-01-02 to 2022-12-30, with calibration done from 2016 to 2018 and a holdout period from 2023 to August 26. The first monthly open rebalance was done with a capped prior variance of 1 and terminal liquidation. The position N1 and monthly win frequency were calculated separately. The fees were 1 basis point per side, with a 5 basis point slip, and a cash yield of 0. The return was 8.995053923013941 percent, with a drawdown of 22.046958036847585 percent, and a win rate of 100 percent over 48 months. The vectorbt daily reconciliation had a maximum error of 1.8189894035458565e-12 at 10,000 initial units. The report notes that there was no 15-minute or whole-share validation, and the data was dividend-adjusted and subject to survivorship bias, but the history was not globally untouched. This is a diagnostic instrument, and there is no winner promotion. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research was done using the research/test_vm.py/results.json file.
A trading strategy called CNXC91106 was tested. The strategy had three main rules: first, it looked at when companies announced important information, and it only considered announcements made before a certain date. It also excluded announcements made on the same day as the trading session, if the exact time of the announcement was not known. Second, the strategy used historical data from 2021 to 2024 to train, and from June 2025 to September 2026 to test. It used a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. The strategy only considered long trades, and it included fees and slippage of 5 basis points and 45 basis points respectively. The results showed that all variants of the strategy had negative returns during the training period, and even the best performing variant during the test period could not make up for the losses. The data used for the strategy came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy had some limitations, including only looking at one company, using historical data, and assuming certain costs. A news scan found the headline was not available, this is a raw script output.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it looked at the dates when companies announce their financial results, and it only traded between 10 days before and 1 day after these announcements, excluding the actual day of the announcement if it was before or after the market closed. Second, it used past data from 2021 to 2024 to train the strategy and data from June 2025 to September 10, 2026, to test it. The strategy was to buy and sell based on certain price movements, with a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. The strategy also considered fees and slippage, which are costs associated with buying and selling, and it reinvested any profits. The results showed that the strategy did not work well, with all versions of the strategy resulting in negative returns. This was true even when looking at a shorter period of time. The data used for this test came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. There were some limitations to the test, including that it only looked at one company, it was based on past data, and it did not account for all possible costs and news events.
A trading strategy called CNXC91106 was tested. The strategy had three main rules. First, it only traded between 10 days before and 1 day before certain events, and it did not trade on the day of the event if it was announced after the market closed or before it opened. Second, the strategy used historical data from 2021 to 2024 to decide when to buy and sell, and it tested its performance using data from June 2025 to September 2026. It stopped trading if it lost a certain amount of money, and it set a target to make three times that amount. If the market opened lower than expected, it stopped trading immediately. The strategy only traded in one direction, which means it only bought and never sold short. It also accounted for the costs of trading, such as fees and the difference between the expected price and the actual price. The strategy was tested in different ways, but none of them made a profit. The data used for the test came from the internet, specifically from the URLs of the companies and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test had some limitations, such as only using data from one company and not accounting for all possible costs and risks.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it used certain dates from the issuer, entering trades 10 days before and exiting the day before the event, excluding events that happened after the market closed or before it opened. It also excluded announcements made on the same day as the trade entry. Second, it used past data from 2021 to 2024 to train the strategy and data from June 2025 to September 2026 to test it. The strategy used a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the trade opened with a gap, it would stop the trade. The strategy only considered long trades, with fees of 5 basis points and slippage of 25 basis points on each side. The returns were compounded and reconciled manually. A news scan found the headline was not needed, instead the strategy used synthetic data for bookkeeping. The strategy was tested with different variants, but all of them had negative returns, so it was not adopted. The data used for the strategy came from individual company URLs and a website. The test had some limitations, including only using data from one company, looking back at past events, and assuming certain costs.
A trading fund manager made notes about a trading strategy called CNXC91106. The strategy has two main rules: first, the manager looks at announcements from companies, but only if they happen before a certain date, and second, the manager uses a special adjustment called SIP to look at daily stock prices for two specific stocks, 1451CNXC and 1470SPY, from 2021 to 2026. The manager uses a computer program called vectorbt to make trades, only buying stocks and paying a fee of 5 basis points and a slippage of 45 basis points for each trade. The manager looks at the returns on investment and the number of trades made, and also considers the drawdown, which is the difference between the highest and lowest values of the investment. The manager uses a special measure called mean excess pp to compare the returns to a benchmark, and tests different variations of the strategy. The manager concludes that the strategy is not working because all the variations have negative returns, and even the positive results in the test period cannot make up for the losses. The manager got the data from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The manager notes that the study has some limitations, including only looking at one company, using old data, and not considering some costs and news events.
A news scan found the text describing the CNXC91106 T-10 time exit train with a 50bp/side condition. The premises of this trading strategy are as follows: first, the strategy uses Freeze2229, which involves entering trades at the open on T-10 and exiting at the close on T-1, excluding event sessions for AMC and BMO, and only considering announcements that predate the entry date. Second, the strategy uses a SIP adjustment with all 1451CNXC and 1470SPY daily bars through 2026-09-10, training from 2021 to 2024, and holding out from June 2025 to 2026. The strategy has a fixed calendar time exit with no stop and is for research control only, not eligible for live policy. Third, the strategy uses vectorbt with long-only orders, fees of 5bp, and slippage of 45bp on each side. The returns and trade counts are reconciled manually, and the initial cash is included in the mark-to-market drawdown. The strategy also uses synthetic two rows per day for bookkeeping, not intraday data, and a fractional capital reinvestment proxy. The mean excess return is the difference against the full T-10/T-1 SPY interval, not exposure-matched alpha. The conclusion is that there will be no adoption of this strategy because all training variants had a negative compounded return, and the positive holdout in T3 cannot rescue the failed train. The sources of the issuer URLs are individually listed in observations 24553-24575, and additional data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations of this strategy include the selection of a single company, retrospective calendar recovery, overlap with 2023 Webhelp, 22 events, possible early news, assumed costs, and a 15-minute and +2R policy that is separate.
A trading fund manager made decisions based on certain premises. The first premise is about a trading strategy called Freeze2229, which involves entering trades 10 days before a certain event and exiting on the day before the event, excluding events that happen after the market closes or before it opens. The manager also considered a system called SIP adjustment, which looked at daily stock prices for CNXC and SPY from 2021 to 2026, with a specific time period set aside for testing. The manager used a system called vectorbt to make trades, only going long, with fees of 5 basis points and slippage of 25 basis points on each side. The manager manually checked the returns and trade counts, and included the initial cash in the calculations. The manager also used a system to track the performance of the trades, comparing it to the performance of the SPY stock over a certain time period. The manager tested different variations of the strategy, but none of them had a positive return, so the manager decided not to adopt the strategy. The manager got the data from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The manager noted some limitations of the study, including that it only looked at one company, used past data, and did not account for all possible costs and news events.
A news scan found the following trading fund notes. The fund manager made three premises. First, the manager considered Freeze2229, which involves the issuer's advance dates, entering at T-10 open and exiting at T-1 close, excluding event sessions for AMC and BMO. Announcements must be made before the entry date, and same-day unknown release times are excluded. Second, the manager made a SIP adjustment using all 1451CNXC and 1470SPY daily bars through 2026-09-10, training from 2021 to 2024, and holding out from June 2025 to 2026. The manager used a fixed calendar time exit with no stop, for research control only, and not for live policy. A stop is a price level that, when reached, automatically closes a trade. Third, the manager used vectorbt, a backtesting framework, with long-only orders, 5 basis points fees, and 25 basis points slippage on each side. Basis points are units of measurement for interest rates and fees. Slippage refers to the difference between expected and actual trade execution prices. The manager reconciled compounded returns and trade counts manually and included initial cash in mark-to-market drawdown. Mark-to-market is a method of valuing assets at current market prices. The manager used synthetic two rows per day for bookkeeping, not intraday data, and fractional capital reinvestment proxy. The mean excess return is the difference against the full T-10/T-1 SPY interval, not exposure-matched alpha. Alpha refers to the excess return of an investment relative to the market. The manager froze Sweep3/5/10 and fixed primary T5, reporting all variants without picking a test winner. The conclusion was no adoption because all training variants had negative compounded returns, and T3 positive holdout could not rescue the failed train. The sources are issuer URLs individually in observations 24553-24575 and https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations include a selected company, retrospective calendar recovery, 2023 Webhelp overlap, 22 events, possible early news, assumed costs, and 15-minute and +2R policy separate.
A news scan found the following trading notes. The trading strategy is based on two premises. First, the strategy involves entering a trade five days before a specific event and exiting the trade one day before the event, excluding events that happen after the market closes or before it opens. The strategy also excludes announcements that are made on the same day as the trade entry, if the release time is unknown. Second, the strategy uses historical data from 2021 to 2024 to train a model, and then tests the model on data from June 2025 to September 10, 2026. The strategy involves setting a stop loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the market opens with a large gap, the stop loss is triggered. The strategy only considers long trades, and it includes fees of 5 basis points and slippage of 45 basis points on each side of the trade. The returns are compounded and reconciled manually. The strategy also includes a bookkeeping adjustment to account for fractional capital reinvestment. The results show that all training variants had negative compounded returns, and even the positive holdout results were not enough to rescue the failed training. The sources of the data are listed individually, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy has several limitations, including the selection of a specific company, the use of retrospective calendar recovery, and the assumption of certain costs. A headline about the trade results mentioned a 50 basis point per side stop and a 3 average daily range target holdout.
A news scan found the following trading notes. The premises of the trade are as follows: the trade is based on an automated system called Freeze2229, which uses issuer advance dates to enter a trade five days before an event and exit the day before the event, excluding events that happen at the start or end of the trading day. The system also excludes announcements that are made on the same day as the trade entry, if the exact release time is unknown. The system was trained on historical data from 2021 to 2024, using daily price bars for the ticker symbols 1451CNXC and 1470SPY up to September 10, 2026. The system uses a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. If the trade opens with a gap, the stop-loss is triggered. The system only considers long trades, with fees of 5 basis points and slippage of 45 basis points on each side. The returns and trade counts are reconciled manually, and the initial cash is included in the mark-to-market drawdown. The system uses a mean excess return metric, which is the difference between the return of the trade and the return of the SPY ticker over the same period. The results of the trade are negative, with all training variants showing a negative compounded return. The system was tested on a holdout period from June 2025 to June 2026, but the results were not positive enough to rescue the failed training period. The sources of the data are listed in observations 24553 to 24575, and are also available at https://runway.adityaperswal.workers.dev/data/stock-bars. The limitations of the system include the selection of a single company, retrospective calendar recovery, overlap with web help in 2023, 22 events, possible early news, assumed costs, and a 15-minute and 2R policy that is separate from the main system.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it would start trading five days before a certain event and stop trading one day before the event, excluding events that happen at the start or end of the trading day. Second, it would use past data from 2021 to 2024 to train the strategy and then test it from June 2025 to September 10, 2026. The strategy would buy a stock and set a stop-loss at the price minus one average daily range and a target at the price plus three average daily ranges. If the stock opened at a bad price, the strategy would stop the trade immediately. The strategy was tested with fees of 5 basis points and slippage of 25 basis points on each trade. The results showed that the strategy did not work well, with all versions of the strategy having negative returns during the training period. Even the version that worked well during the test period could not make up for the losses during training. The data used for the test came from the internet and a website called runway.adityaperswal.workers.dev. There were some limitations to the test, including only using data from one company and not accounting for all possible costs and news events.
A trading strategy called CNXC91106 was tested. The strategy had two main rules: first, it looked at the dates when companies announced their financial results, and it started trading five days before these announcements and stopped one day before. It avoided trading on the days of the announcements if they happened before or after the market closed. Second, it used past data from 2021 to 2024 to train the strategy and data from 2025 to June 2026 to test it. The strategy was tested on a specific stock, CNXC, and compared to the performance of the SPY stock index. The goal was to make money by buying the stock, with a stop-loss at 1 average daily range below the entry price and a target at 3 average daily ranges above the entry price. A stop-loss is a price at which to automatically sell a stock to limit losses, and a target is a price at which to automatically sell a stock to secure profits. The average daily range is the difference between the highest and lowest prices of a stock in a day. The strategy also accounted for fees and slippage, which is the difference between the expected price of a trade and the actual price. The results showed that the strategy did not work well, with all versions of the strategy resulting in negative returns. This means that the strategy lost money. The data used for the test came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test had some limitations, including only looking at one company, using past data, and not accounting for all possible costs and news events.
This is a report about a trading strategy called CNXC91106. The strategy is based on two main ideas: first, to buy and sell stocks based on certain dates, and second, to adjust the strategy using historical data from two stocks, 1451CNXC and 1470SPY, from 2021 to 2026. The strategy was tested using a computer program called vectorbt, which simulated buying and selling with certain fees and slippage, meaning the difference between expected and actual prices. The results showed that the strategy did not work well, with negative returns in all training variants, and even a positive result in one test period could not make up for the overall failure. The data used for this strategy came from individual company websites and a website called https://runway.adityaperswal.workers.dev/data/stock-bars. There are some limitations to this strategy, including that it only looked at one company, used past data, and did not account for all possible costs and news events.
A news scan found the following text: Premises for the CNXC91106 T-5 time exit train with 50 basis points per side are as follows. The first premise is called Freeze2229, which involves the issuer's advance dates, entering at T-5 open and exiting at T-1 close, excluding event sessions for after-market close and before-market open announcements. Announcements must be made before the entry date, and same-day unknown release times are excluded. The second premise involves a SIP adjustment using all 1451CNXC and 1470SPY daily bars through 2026-09-10, training from 2021 to 2024, and a chronological holdout from June 2025 to 2026. The exit is based on a fixed calendar time with no stop, and is for research control only, not eligible for live policy. The third premise uses vectorbt with long-only orders, fees of 5 basis points, and slippage of 45 basis points on each side. Compounded returns and trade counts are reconciled manually, and initial cash is included in the mark-to-market drawdown. Synthetic two rows per day are for bookkeeping and are not intraday data. Fractional capital reinvestment is used as a proxy, and adjusted prices are not raw executable stops. Mean excess return is the difference against the full T-5/T-1 SPY interval, not exposure-matched alpha. The conclusion is that there will be no adoption because all training variants had negative compounded returns, and a positive holdout in T3 cannot rescue a failed train. Sources include issuer URLs individually in observations 24553-24575 and https://runway.adityaperswal.workers.dev/data/stock-bars. Limitations include a selected company, retrospective calendar recovery, 2023 Web help overlap, 22 events, possible early news, costs assumed, and 15-minute and +2R policy separate.
A trading fund manager made notes about a trading strategy called CNXC91106. The strategy has three main points: first, it uses certain dates to decide when to buy and sell, excluding days when important events happen before the market opens or closes, and only considering announcements made before the trading day starts. Second, the strategy uses historical data from 2021 to 2024 to train, and from June 2025 to September 10, 2026, to test, with a fixed exit time and no stop-loss, and it only considers buying, not selling, with fees of 5 basis points and slippage of 25 basis points on each side. Third, the strategy uses a method called vectorbt to make trades, and it calculates returns and trade counts manually, including the initial cash in the calculations, and it uses a proxy for reinvesting capital. The strategy compares its performance to the performance of the SPY stock, and it reports the results of different variants without choosing a winner. The conclusion is that the strategy did not work well, with all variants having negative returns during the training period, and even the best variant during the testing period could not make up for the losses. The sources of the data are listed, including a website, and the limitations of the strategy are noted, including the use of data from only one company, and the possible effects of early news and costs.