Recovery Staking Plan
The Recovery Staking Plan is setup to recover your losses over a set number of cycles and bets. At default we have 2 cycles and 4 bets. (Each cycle contains 4 bets). This allows us 2 chances to recover our losses before calling it quits and starting a new series.
Initial Stake: Set as a percentage of the starting bankroll. For instance, with a £100 bankroll and a 2% stake, the initial bet would be £2.
Cycles and Bets Configuration: Users can define the number of cycles (up to 25) and bets per cycle (up to 100) to tailor the recovery process to their risk tolerance and strategy.
How Does The Recovery Staking Plan Work ?
Upon a loss, the staking plan calculates the total deficit. The deficit is divided by the number of bets in the cycle to determine the additional amount to be added to each subsequent stake.
Stake Calculation Example:
- Initial bet of £2 results in a loss, creating a £2 deficit.
- With a cycle set to recover over 4 bets, each subsequent stake increases by £0.50 (£2 deficit ÷ 4 bets).
- If all 4 bets win, the deficit is recovered, and the stake reverts to the initial £2.
Multiple Cycles :
If a loss occurs before completing a cycle, the plan progresses to the next cycle, recalculating stakes to recover the remaining deficit over the new cycle. For example, a loss in the fourth bet with a remaining deficit of £1.50 would be divided over the next cycle’s 4 bets, adding £0.38 to each stake.
Cycle Completion:
If the deficit is not recovered after the designated cycles, the plan resets, accepting the loss and starting anew with the initial stake.
Available Settings in TSM:
Recover Initial Stake: An option to include the initial stake in the recovery calculation, aiming to recuperate both the loss and the original stake amount in each bet.
Link to Cumulative Total: Adjusts the initial stake based on the cumulative bankroll, allowing stakes to scale with the bankroll’s performance.
Commission Recovery: Incorporates the recovery of commission fees into the staking plan, ensuring net profits are accurately targeted.