aboutsummaryrefslogtreecommitdiff
path: root/src/wallet/coinselection.cpp
blob: 513572da455821eb6e2bee5f4ed3f0458f6b6197 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
// Copyright (c) 2017-2021 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.

#include <wallet/coinselection.h>

#include <consensus/amount.h>
#include <policy/feerate.h>
#include <util/check.h>
#include <util/system.h>
#include <util/moneystr.h>

#include <numeric>
#include <optional>

namespace wallet {
// Descending order comparator
struct {
    bool operator()(const OutputGroup& a, const OutputGroup& b) const
    {
        return a.GetSelectionAmount() > b.GetSelectionAmount();
    }
} descending;

/*
 * This is the Branch and Bound Coin Selection algorithm designed by Murch. It searches for an input
 * set that can pay for the spending target and does not exceed the spending target by more than the
 * cost of creating and spending a change output. The algorithm uses a depth-first search on a binary
 * tree. In the binary tree, each node corresponds to the inclusion or the omission of a UTXO. UTXOs
 * are sorted by their effective values and the tree is explored deterministically per the inclusion
 * branch first. At each node, the algorithm checks whether the selection is within the target range.
 * While the selection has not reached the target range, more UTXOs are included. When a selection's
 * value exceeds the target range, the complete subtree deriving from this selection can be omitted.
 * At that point, the last included UTXO is deselected and the corresponding omission branch explored
 * instead. The search ends after the complete tree has been searched or after a limited number of tries.
 *
 * The search continues to search for better solutions after one solution has been found. The best
 * solution is chosen by minimizing the waste metric. The waste metric is defined as the cost to
 * spend the current inputs at the given fee rate minus the long term expected cost to spend the
 * inputs, plus the amount by which the selection exceeds the spending target:
 *
 * waste = selectionTotal - target + inputs × (currentFeeRate - longTermFeeRate)
 *
 * The algorithm uses two additional optimizations. A lookahead keeps track of the total value of
 * the unexplored UTXOs. A subtree is not explored if the lookahead indicates that the target range
 * cannot be reached. Further, it is unnecessary to test equivalent combinations. This allows us
 * to skip testing the inclusion of UTXOs that match the effective value and waste of an omitted
 * predecessor.
 *
 * The Branch and Bound algorithm is described in detail in Murch's Master Thesis:
 * https://murch.one/wp-content/uploads/2016/11/erhardt2016coinselection.pdf
 *
 * @param const std::vector<CInputCoin>& utxo_pool The set of UTXOs that we are choosing from.
 *        These UTXOs will be sorted in descending order by effective value and the CInputCoins'
 *        values are their effective values.
 * @param const CAmount& selection_target This is the value that we want to select. It is the lower
 *        bound of the range.
 * @param const CAmount& cost_of_change This is the cost of creating and spending a change output.
 *        This plus selection_target is the upper bound of the range.
 * @returns The result of this coin selection algorithm, or std::nullopt
 */

static const size_t TOTAL_TRIES = 100000;

std::optional<SelectionResult> SelectCoinsBnB(std::vector<OutputGroup>& utxo_pool, const CAmount& selection_target, const CAmount& cost_of_change)
{
    SelectionResult result(selection_target);
    CAmount curr_value = 0;

    std::vector<bool> curr_selection; // select the utxo at this index
    curr_selection.reserve(utxo_pool.size());

    // Calculate curr_available_value
    CAmount curr_available_value = 0;
    for (const OutputGroup& utxo : utxo_pool) {
        // Assert that this utxo is not negative. It should never be negative, effective value calculation should have removed it
        assert(utxo.GetSelectionAmount() > 0);
        curr_available_value += utxo.GetSelectionAmount();
    }
    if (curr_available_value < selection_target) {
        return std::nullopt;
    }

    // Sort the utxo_pool
    std::sort(utxo_pool.begin(), utxo_pool.end(), descending);

    CAmount curr_waste = 0;
    std::vector<bool> best_selection;
    CAmount best_waste = MAX_MONEY;

    // Depth First search loop for choosing the UTXOs
    for (size_t i = 0; i < TOTAL_TRIES; ++i) {
        // Conditions for starting a backtrack
        bool backtrack = false;
        if (curr_value + curr_available_value < selection_target ||                // Cannot possibly reach target with the amount remaining in the curr_available_value.
            curr_value > selection_target + cost_of_change ||    // Selected value is out of range, go back and try other branch
            (curr_waste > best_waste && (utxo_pool.at(0).fee - utxo_pool.at(0).long_term_fee) > 0)) { // Don't select things which we know will be more wasteful if the waste is increasing
            backtrack = true;
        } else if (curr_value >= selection_target) {       // Selected value is within range
            curr_waste += (curr_value - selection_target); // This is the excess value which is added to the waste for the below comparison
            // Adding another UTXO after this check could bring the waste down if the long term fee is higher than the current fee.
            // However we are not going to explore that because this optimization for the waste is only done when we have hit our target
            // value. Adding any more UTXOs will be just burning the UTXO; it will go entirely to fees. Thus we aren't going to
            // explore any more UTXOs to avoid burning money like that.
            if (curr_waste <= best_waste) {
                best_selection = curr_selection;
                best_selection.resize(utxo_pool.size());
                best_waste = curr_waste;
                if (best_waste == 0) {
                    break;
                }
            }
            curr_waste -= (curr_value - selection_target); // Remove the excess value as we will be selecting different coins now
            backtrack = true;
        }

        // Backtracking, moving backwards
        if (backtrack) {
            // Walk backwards to find the last included UTXO that still needs to have its omission branch traversed.
            while (!curr_selection.empty() && !curr_selection.back()) {
                curr_selection.pop_back();
                curr_available_value += utxo_pool.at(curr_selection.size()).GetSelectionAmount();
            }

            if (curr_selection.empty()) { // We have walked back to the first utxo and no branch is untraversed. All solutions searched
                break;
            }

            // Output was included on previous iterations, try excluding now.
            curr_selection.back() = false;
            OutputGroup& utxo = utxo_pool.at(curr_selection.size() - 1);
            curr_value -= utxo.GetSelectionAmount();
            curr_waste -= utxo.fee - utxo.long_term_fee;
        } else { // Moving forwards, continuing down this branch
            OutputGroup& utxo = utxo_pool.at(curr_selection.size());

            // Remove this utxo from the curr_available_value utxo amount
            curr_available_value -= utxo.GetSelectionAmount();

            // Avoid searching a branch if the previous UTXO has the same value and same waste and was excluded. Since the ratio of fee to
            // long term fee is the same, we only need to check if one of those values match in order to know that the waste is the same.
            if (!curr_selection.empty() && !curr_selection.back() &&
                utxo.GetSelectionAmount() == utxo_pool.at(curr_selection.size() - 1).GetSelectionAmount() &&
                utxo.fee == utxo_pool.at(curr_selection.size() - 1).fee) {
                curr_selection.push_back(false);
            } else {
                // Inclusion branch first (Largest First Exploration)
                curr_selection.push_back(true);
                curr_value += utxo.GetSelectionAmount();
                curr_waste += utxo.fee - utxo.long_term_fee;
            }
        }
    }

    // Check for solution
    if (best_selection.empty()) {
        return std::nullopt;
    }

    // Set output set
    for (size_t i = 0; i < best_selection.size(); ++i) {
        if (best_selection.at(i)) {
            result.AddInput(utxo_pool.at(i));
        }
    }
    result.ComputeAndSetWaste(CAmount{0});
    assert(best_waste == result.GetWaste());

    return result;
}

std::optional<SelectionResult> SelectCoinsSRD(const std::vector<OutputGroup>& utxo_pool, CAmount target_value)
{
    SelectionResult result(target_value);

    std::vector<size_t> indexes;
    indexes.resize(utxo_pool.size());
    std::iota(indexes.begin(), indexes.end(), 0);
    Shuffle(indexes.begin(), indexes.end(), FastRandomContext());

    CAmount selected_eff_value = 0;
    for (const size_t i : indexes) {
        const OutputGroup& group = utxo_pool.at(i);
        Assume(group.GetSelectionAmount() > 0);
        selected_eff_value += group.GetSelectionAmount();
        result.AddInput(group);
        if (selected_eff_value >= target_value) {
            return result;
        }
    }
    return std::nullopt;
}

static void ApproximateBestSubset(const std::vector<OutputGroup>& groups, const CAmount& nTotalLower, const CAmount& nTargetValue,
                                  std::vector<char>& vfBest, CAmount& nBest, int iterations = 1000)
{
    std::vector<char> vfIncluded;

    vfBest.assign(groups.size(), true);
    nBest = nTotalLower;

    FastRandomContext insecure_rand;

    for (int nRep = 0; nRep < iterations && nBest != nTargetValue; nRep++)
    {
        vfIncluded.assign(groups.size(), false);
        CAmount nTotal = 0;
        bool fReachedTarget = false;
        for (int nPass = 0; nPass < 2 && !fReachedTarget; nPass++)
        {
            for (unsigned int i = 0; i < groups.size(); i++)
            {
                //The solver here uses a randomized algorithm,
                //the randomness serves no real security purpose but is just
                //needed to prevent degenerate behavior and it is important
                //that the rng is fast. We do not use a constant random sequence,
                //because there may be some privacy improvement by making
                //the selection random.
                if (nPass == 0 ? insecure_rand.randbool() : !vfIncluded[i])
                {
                    nTotal += groups[i].GetSelectionAmount();
                    vfIncluded[i] = true;
                    if (nTotal >= nTargetValue)
                    {
                        fReachedTarget = true;
                        if (nTotal < nBest)
                        {
                            nBest = nTotal;
                            vfBest = vfIncluded;
                        }
                        nTotal -= groups[i].GetSelectionAmount();
                        vfIncluded[i] = false;
                    }
                }
            }
        }
    }
}

std::optional<SelectionResult> KnapsackSolver(std::vector<OutputGroup>& groups, const CAmount& nTargetValue)
{
    SelectionResult result(nTargetValue);

    // List of values less than target
    std::optional<OutputGroup> lowest_larger;
    std::vector<OutputGroup> applicable_groups;
    CAmount nTotalLower = 0;

    Shuffle(groups.begin(), groups.end(), FastRandomContext());

    for (const OutputGroup& group : groups) {
        if (group.GetSelectionAmount() == nTargetValue) {
            result.AddInput(group);
            return result;
        } else if (group.GetSelectionAmount() < nTargetValue + MIN_CHANGE) {
            applicable_groups.push_back(group);
            nTotalLower += group.GetSelectionAmount();
        } else if (!lowest_larger || group.GetSelectionAmount() < lowest_larger->GetSelectionAmount()) {
            lowest_larger = group;
        }
    }

    if (nTotalLower == nTargetValue) {
        for (const auto& group : applicable_groups) {
            result.AddInput(group);
        }
        return result;
    }

    if (nTotalLower < nTargetValue) {
        if (!lowest_larger) return std::nullopt;
        result.AddInput(*lowest_larger);
        return result;
    }

    // Solve subset sum by stochastic approximation
    std::sort(applicable_groups.begin(), applicable_groups.end(), descending);
    std::vector<char> vfBest;
    CAmount nBest;

    ApproximateBestSubset(applicable_groups, nTotalLower, nTargetValue, vfBest, nBest);
    if (nBest != nTargetValue && nTotalLower >= nTargetValue + MIN_CHANGE) {
        ApproximateBestSubset(applicable_groups, nTotalLower, nTargetValue + MIN_CHANGE, vfBest, nBest);
    }

    // If we have a bigger coin and (either the stochastic approximation didn't find a good solution,
    //                                   or the next bigger coin is closer), return the bigger coin
    if (lowest_larger &&
        ((nBest != nTargetValue && nBest < nTargetValue + MIN_CHANGE) || lowest_larger->GetSelectionAmount() <= nBest)) {
        result.AddInput(*lowest_larger);
    } else {
        for (unsigned int i = 0; i < applicable_groups.size(); i++) {
            if (vfBest[i]) {
                result.AddInput(applicable_groups[i]);
            }
        }

        if (LogAcceptCategory(BCLog::SELECTCOINS)) {
            std::string log_message{"Coin selection best subset: "};
            for (unsigned int i = 0; i < applicable_groups.size(); i++) {
                if (vfBest[i]) {
                    log_message += strprintf("%s ", FormatMoney(applicable_groups[i].m_value));
                }
            }
            LogPrint(BCLog::SELECTCOINS, "%stotal %s\n", log_message, FormatMoney(nBest));
        }
    }

    return result;
}

/******************************************************************************

 OutputGroup

 ******************************************************************************/

void OutputGroup::Insert(const CInputCoin& output, int depth, bool from_me, size_t ancestors, size_t descendants, bool positive_only) {
    // Compute the effective value first
    const CAmount coin_fee = output.m_input_bytes < 0 ? 0 : m_effective_feerate.GetFee(output.m_input_bytes);
    const CAmount ev = output.txout.nValue - coin_fee;

    // Filter for positive only here before adding the coin
    if (positive_only && ev <= 0) return;

    m_outputs.push_back(output);
    CInputCoin& coin = m_outputs.back();

    coin.m_fee = coin_fee;
    fee += coin.m_fee;

    coin.m_long_term_fee = coin.m_input_bytes < 0 ? 0 : m_long_term_feerate.GetFee(coin.m_input_bytes);
    long_term_fee += coin.m_long_term_fee;

    coin.effective_value = ev;
    effective_value += coin.effective_value;

    m_from_me &= from_me;
    m_value += output.txout.nValue;
    m_depth = std::min(m_depth, depth);
    // ancestors here express the number of ancestors the new coin will end up having, which is
    // the sum, rather than the max; this will overestimate in the cases where multiple inputs
    // have common ancestors
    m_ancestors += ancestors;
    // descendants is the count as seen from the top ancestor, not the descendants as seen from the
    // coin itself; thus, this value is counted as the max, not the sum
    m_descendants = std::max(m_descendants, descendants);
}

bool OutputGroup::EligibleForSpending(const CoinEligibilityFilter& eligibility_filter) const
{
    return m_depth >= (m_from_me ? eligibility_filter.conf_mine : eligibility_filter.conf_theirs)
        && m_ancestors <= eligibility_filter.max_ancestors
        && m_descendants <= eligibility_filter.max_descendants;
}

CAmount OutputGroup::GetSelectionAmount() const
{
    return m_subtract_fee_outputs ? m_value : effective_value;
}

CAmount GetSelectionWaste(const std::set<CInputCoin>& inputs, CAmount change_cost, CAmount target, bool use_effective_value)
{
    // This function should not be called with empty inputs as that would mean the selection failed
    assert(!inputs.empty());

    // Always consider the cost of spending an input now vs in the future.
    CAmount waste = 0;
    CAmount selected_effective_value = 0;
    for (const CInputCoin& coin : inputs) {
        waste += coin.m_fee - coin.m_long_term_fee;
        selected_effective_value += use_effective_value ? coin.effective_value : coin.txout.nValue;
    }

    if (change_cost) {
        // Consider the cost of making change and spending it in the future
        // If we aren't making change, the caller should've set change_cost to 0
        assert(change_cost > 0);
        waste += change_cost;
    } else {
        // When we are not making change (change_cost == 0), consider the excess we are throwing away to fees
        assert(selected_effective_value >= target);
        waste += selected_effective_value - target;
    }

    return waste;
}

void SelectionResult::ComputeAndSetWaste(CAmount change_cost)
{
    m_waste = GetSelectionWaste(m_selected_inputs, change_cost, m_target, m_use_effective);
}

CAmount SelectionResult::GetWaste() const
{
    return *Assert(m_waste);
}

CAmount SelectionResult::GetSelectedValue() const
{
    return std::accumulate(m_selected_inputs.cbegin(), m_selected_inputs.cend(), CAmount{0}, [](CAmount sum, const auto& coin) { return sum + coin.txout.nValue; });
}

void SelectionResult::Clear()
{
    m_selected_inputs.clear();
    m_waste.reset();
}

void SelectionResult::AddInput(const OutputGroup& group)
{
    util::insert(m_selected_inputs, group.m_outputs);
    m_use_effective = !group.m_subtract_fee_outputs;
}

const std::set<CInputCoin>& SelectionResult::GetInputSet() const
{
    return m_selected_inputs;
}

std::vector<CInputCoin> SelectionResult::GetShuffledInputVector() const
{
    std::vector<CInputCoin> coins(m_selected_inputs.begin(), m_selected_inputs.end());
    Shuffle(coins.begin(), coins.end(), FastRandomContext());
    return coins;
}

bool SelectionResult::operator<(SelectionResult other) const
{
    Assert(m_waste.has_value());
    Assert(other.m_waste.has_value());
    // As this operator is only used in std::min_element, we want the result that has more inputs when waste are equal.
    return *m_waste < *other.m_waste || (*m_waste == *other.m_waste && m_selected_inputs.size() > other.m_selected_inputs.size());
}
} // namespace wallet